My simple definition goes like this: machine learning allows a computer to learn from experience. That may sound trivial, but if you break that definition down, it has profound implications. Before machine learning, computers couldn't improve from experience. Instead, whatever the code said is what the computer did.
This tutorial firstly explains What is Machine Learning with best examples in 2019. This tutorial explains Artificial Intelligence vs. Machine Learning vs Neural Networks in detail with examples.
Machine learning, in its simplest explanation, involves allowing a computer to vary its responses and introducing a feedback loop for good and bad responses. This means that machine learning algorithms are fundamentally different from the computer programs that have come before them.
Understanding the difference between explicit programming and algorithm training is the first step to seeing how machine learning fundamentally changes computer science.
With a few recent exceptions, nearly every piece of software you've used in your life has been explicitly programmed. That means that some human wrote a set of rules for the computer to follow.
Everything from your computer's operating system to the internet, to apps on your phone, has code that a human wrote. Without humans giving a computer a set of rules to act, the computer wouldn't be able to do anything.
Explicit programming is great. It's the backbone for everything we currently do with computers. It's ideal for when you need a computer to manage data, calculate a value, or keep track of relationships for you. Explicit programming is very powerful, but it has a bottleneck: the human.
This gets to be problematic when we want to do complex things with a computer, like asking it to recognize a photo of a cat. If we were to use explicit programming to teach a computer what to look for in a cat, we'd spend years writing code for every contingency.
What if you can't see all four legs in the photo? What if the cat is a different color? Could the computer pick out a black cat on a black background or a white cat in the snow?
These are all things we take for granted as humans. Our brains recognize things quickly and easily in many contexts. Computers aren't so good at that, and it would take millions of lines of explicit code to tell a computer how to identify a cat.
In fact, it may not be possible at all to explicitly program a computer to 100% accurately identify cats, because context can always change and mess up your code.
This is where algorithms come into play. With explicit programming, we were trying to tell the computer what a cat is and make allowances for every contingency in our code. In contrast, machine learning algorithms allow the computer to discover what a cat is.
To start, the algorithm might contain a few key features. For instance, we might tell the computer to look for four legs and a tail. Then, we feed the algorithm many pictures.
Some of the pictures are cats, but others may be dogs, trees, or random images. When the algorithm makes a guess, we'll reinforce correct guesses and give negative feedback for incorrect guesses.
Over time, the computer will use the algorithm to build its own model of what to look for to identify a cat. The components in the computer's model may be things we didn't even think of at first.
With more reinforcement and thousands of images, the algorithm will gradually become better at identifying cats. It may never reach 100% accuracy, but it will be accurate enough to replace a human cat image labeler and be more efficient.
Algorithms are guidelines but they aren't explicit rules. They're a new way of telling a computer how to approach a task. They introduce feedback loops that self-correct over the course of hundreds or thousands of trials at a task.
This blog is about machine learning, but that term fits within a larger context. Since machine learning is growing in popularity, it's getting a lot of news coverage. In those articles, journalists often use the terms artificial intelligence, machine learning, and neural networks interchangeably. However, there are slight variations between the three terms.
Artificial intelligence is the oldest and broadest of the three terms. Coined in the middle of the 20th century, artificial intelligence refers to any time a machine observes and responds to its environment. Artificial intelligence stands in contrast to natural intelligence in humans and animals.
Over time, however, the scope of artificial intelligence has changed. For instance, character recognition used to be a major challenge for AI. Now, it's routine and no longer considered part of AI.
As we discover new uses for AI, we integrate them into our frame of reference for what's normal, and the scope of AI extends to whatever the next new thing is.
Machine learning is a specific subset of AI. We've already spent some time defining it in this blog, but it refers to giving a machine a feedback loop that allows it to learn from experience.
As a term, machine learning has only been around since the 1980s. Only recently, in the past 10-15 years have we had the processing and data storage power to really start implementing machine learning at scale.
Neural networks are a subset of machine learning, and they're the hottest trend in the industry right now. A neural network consists of many nodes that work together to produce an answer.
Each of the lowest nodes has a specific function. For example, when looking at an image the low-level nodes might identify specific colors or lines. Later nodes might group the lines into shapes, measure distances, or look for color density.
Each of these nodes then gets weighted for its impact on the final answer. Early on, the neural network will make a lot of mistakes, but over the course of many trials, it will update the weighting of each node to get better at finding the correct answer.
Now, when you read an article about AI, machine learning, or neural networks, you'll understand the difference. The key is to realize that they're subsets. Neural networks are just one type of machine learning which in turn is only part of artificial intelligence.
Machine learning is useful anywhere you need to recognize patterns and predict behavior based on historical data. Recognizing patterns could mean anything from character recognition to predictive maintenance to recommending products to customers based on past purchases.
However, the computer doesn't inherently understand the data or the problem. Instead, a data scientist has to teach the computer what to look for using proper feedback. If the data scientist doesn't define the problem well, even the best algorithm trained on the biggest dataset won't yield the results you want.
It's clear that machine learning is not yet well suited to high-level, symbolic reasoning. For example, an algorithm may be able to identify a basket, colorful eggs, and a field, but it wouldn't be able to say that's an Easter Egg hunt like most humans would.
Typically, machine learning projects have a very narrow, specific problem to which they're finding an answer. A different problem will require a new approach and possibly a different algorithm.
Machine learning is possible at scale because of the amount of data we've begun collecting over the past several years. This big data revolution is the key that has unlocked complex algorithm training. Data is at the core of tuning a machine learning algorithm to give the right response.
Since data is so central to machine learning, the results are a direct reflection of the inputs. If there's a bias within the data, the machine learning algorithm will learn to be biased.
For example, applicant hiring predictors, court sentencing recommendations, and medical diagnosis are all using machine learning, and they all have some level of cultural, gender, race, education, or other bias built into the datasets that train them.
Bias extends beyond prejudice in data collection. Sometimes data misleads an algorithm in other ways. Consider the case of a military machine learning model trained to look for camouflaged tanks in a forest. The data scientists trained the algorithm on a set of pictures, some of which had tanks in the trees and others that just had trees alone.
After training, the model scored nearly perfect accuracy on the tests the data scientists ran. However, when the model entered production, it didn't work at all to identify tanks.
It turns out that in the training dataset, the pictures of tanks were taken on a sunny day, while the forest-only pictures were taken on a cloudy day. The algorithm had learned to identify sunny vs. cloudy days, not tanks!
No dataset is perfect, but we can take precautions to make them less biased. The key precautions come from statistics. When possible, data should be a random sample of the target population.
The sample size should be large enough that you can draw meaningful conclusions from the results with a high level of confidence. Data should be accurately labeled and cleaned for bad/outlying data points that could mislead the algorithm.
We have an entire blog to come on data, where we'll explore these issues in greater depth.
Algorithms are the major component that people think of when they refer to machine learning. This is the actual code that tells the computer what to look for and how to adjust its weighting of possible answers based on the responses it receives.
There are many well-established machine learning algorithms at this point.
Many of these come pre-loaded into popular data science coding libraries. Creating a basic machine learning model is as simple as testing multiple pre-created algorithms to see which fits the data best. Each model has its own strengths, weaknesses, architecture, and unique approach to weighting results.
If you're a programmer reading this blog and thinking about getting into machine learning, don't make the mistake of writing algorithms from scratch. Eventually, yes, any good machine learning expert will need to know how to write an algorithm. However, the off-the-shelf algorithms are becoming industry standards and they work in 80+% of use cases.
Writing an algorithm from scratch requires significant math, theory, and coding skills. We'll have a whole blog on algorithms and how they work, as well. Suffice it to say that the algorithms are the key to a working machine learning model.
Training an algorithm on a dataset is where the magic happens in machine learning. It's the part where the machine actually learns. It's also the part where machine learning can become resource intensive.
If you're trying to do something complex or train an algorithm on a huge dataset, it could take time and significant computing power to get the results you want.
Training also generally comes with diminishing returns. For a given task with a yes/no answer, you can likely get to 80% accuracy with a small amount of training. To get to 90% would take much longer.
95% even longer, and each additional percentage of model accuracy you want the more training (and training data) you'll need. This algorithm tuning for accuracy is a major part of a data scientist's job.
Typically, machine learning training is static, meaning you can't train the model in real time. This means that the model is either in training or in production. With more use in production, the model doesn't get better. If you want to improve the model, you'll have to retrain it separately.
However, it is possible to dynamically train a model. These applications are much more difficult and expensive to implement. They also require that you constantly monitor the real-time data that the algorithm is receiving. The upside, of course, is the model remains responsive to incoming data and doesn't go out of date over time.
Another challenge is that during the training phase, the algorithm looks for correlation, not causation. A great example of this is the military tank camouflage detector I mentioned above. The algorithm found that cloudy days were correlated with getting the right result.
Training teaches the algorithm to look for the right result, even at the expense of the right reasons. This is cool when machine learning points out a variable that correlates to correct results that we hadn't previously thought to look for. It's problematic when that correlation turns out to be a false positive of some kind.
We'll also have a full blog on algorithm training later in this blog. This blog is just an outline of the basic concepts to get us started.
The final, often overlooked, the step of machine learning is presenting the results. The goal of machine learning is to produce useful data for human beings.
There's a lot of work a data scientist must do to explain the context, problem, and solution of a machine learning application. Aside from answering how and why the model works, the data scientists also need to present results in a way that's accessible to the end audience.
In the case of Gmail's spam filter, that means demonstrating the spam-reducing value of the machine learning filter and building an integration for the model into the Gmail platform. For Amazon product recommendations, that means testing the results of the model in the real world.
Often, the act of preparing and using the results will uncover something that was missing in the original model. Thus, machine learning projects are often iterative, adding more functionality and combining various models over time to fit the needs of human beings in the real world.
Machine learning can be supervised, unsupervised, or semi-supervised. The various categories depending on the type of data and your goals for what to do with that data.
With supervised learning, the algorithm needs a training dataset that's labeled with the correct answers in order to learn. Those labels act as a teacher supervising the learning.
As the algorithm makes guesses about whether or not there is a cat in the picture, the teacher's feedback (the labels) will help the model tune itself. The model stops learning when it reaches an acceptable level of accuracy or runs out of labeled training data.
Supervised learning is great for tasks where the model needs to predict outcomes. These prediction problems could involve using statistics to guess a value (e.g. 20 kgs, $1,498, .08 cm) or categorizing data based on given classifications (e.g. "cat," "green," "happy").
We use the term unsupervised learning when the training dataset doesn't have labels with a correct answer. Instead, we allow the algorithm to draw its own conclusions by comparing the data to itself. The goal is to find out something about the underlying structure or distribution of the dataset.
Unsupervised learning can be used for clustering problems, where the data ought to be organized into groups that are similar. We can also use it for association problems to find out which variables correlate with each other.
In many cases, only part of the dataset is labeled, and that's where semi-structured learning comes in. When a majority of the dataset is unlabelled, usually due to the cost of hiring humans to label the data, we can still use a combination of supervised and unsupervised techniques to draw conclusions from the data.
Unsupervised learning can help us with the structure and distribution of the dataset. Then, we can use the few labels we do have as supervised training data. If we use that data on the rest of the dataset, we could potentially use the results as training data themselves for a new model.
Basically any application that involves classification, prediction, or detecting anomalies based on a large dataset is a potential use for machine learning.
Machine learning is rapidly entering every aspect of our lives and over the coming years will be a foundational technology in society, in some ways like the Internet today.
If you read about machine learning, especially neural networks and deep learning, you'll likely hear references to machine learning being a "black box" model. When we talk about black boxes, we mean that the inner workings of the model aren't exactly clear.
For example, the human brain is a black box decision maker (at least at this moment in history). We know certain parts of the brain are responsible for certain life functions. However, we don't really understand how the brain processes inputs and sends signals around to create thoughts and actions (outputs).
Similar complexity applies to some machine learning algorithms, especially ones that involve multiple layers of neural nodes or complex relationships between many variables. It can be difficult to explain, in a human way, what the algorithm is doing and why it works.
Of course, this black box terminology is somewhat of a misnomer in machine learning. We can, in fact, understand the architecture, patterns, and weights of the different nodes in an algorithm. Therefore we can look inside the black box. However, what we find there might not make any rational sense to us as humans.
Not even the foremost experts in the world can explain why a machine learning model has weighted and combined various factors the way it has, and in many ways, it's highly dependent on the dataset the model was trained on.
It's possible that an algorithm trained on a different training dataset could create a completely different model that still generates similar results.
To clarify, it's helpful to think of machine learning algorithms (in supervised learning scenarios) as searching for a function such that f(input) = output.
When we use machine learning to model that function, the function is usually messy, complex, and we may not fully understand all the relevant properties of the function. Machine learning allows us to say exactly what the function is, but we may not be able to comprehend what the function does or why it does it.
In that sense, machine learning models can have black box problems where they're too complex to understand. But the entire field of machine learning is not necessarily a black box.
Still, the fact that we sometimes can't understand and explain the results of machine learning is troubling. As quickly as the adoption of this technology is growing, machine learning is entering parts of our lives that have deep, lasting consequences.
When a black box predicts treatment plans for diseases, runs an airplane's autopilot, or determines jail sentences, do we want to be sure we understand how those decisions are being made? Or do we trust the machines and the scientists behind the algorithms to look out for our best interests?
This is an ongoing debate at the heart of the machine learning revolution. On the one hand, trusting the algorithms and models could lead to saving lives, greater prosperity, and scientific achievements.
However, the tradeoff in transparency is real. We won't be able to definitively say why our predictions are correct, only that the algorithm believes there's a 97.2% chance that they are.
I don't have an answer that can neatly tie up this debate. Instead, you'll have to form your own opinions based on the benefits and drawbacks you see in machine learning throughout this blog and other reading. If you're interested in this problem, I recommend the MIT Technology Review's "The Dark Secret at the Heart of AI" article (available online) to start learning more.
Hopefully, this blog has given an easy-to-digest, broad overview of how everything fits together and what to expect from each component blog. In the following blogs, we'll dig deeper into the nuts and bolts of machine learning.
After a data scientist defines a problem they'd like to solve, the first step in any machine learning adventure is finding a dataset that to work with. That may be harder than it sounds at first.
While we're certainly living in the era of big data, finding clean data that are well-labeled for supervised learning with the necessary variables could be a challenge.
Choosing the right dataset, and having enough data for training, is critical to the success of a machine learning project. Data that are skewed or incomplete could lead to creating a machine learning model that's biased or outright useless.
The good news is there's a lot of potential data out there. Usually, when a data scientist works in a corporate setting, the company will already have some data that they want to be analyzed. This corporate data may also need to be linked to data from public sources.
For instance, satellite imagery from Landsat gets updated daily on Amazon Web Services, and you could track construction or deforestation with a machine learning algorithm. Open source mapping from OpenStreetMap could form the basis of a customer mapping problem.
Information from the U.S. Census can give demographic information about an area. You can find human genomes sequenced and available to investigate genetic variation. Deutsche Bank releases real-time financial market data that would enable a machine learning project about market trends.
There's no shortage of potential projects. But before using all this data, data scientists have to be sure it meets a few criteria.
This is fairly straightforward, but failure to remove bad values will impact model performance. The first step to cleaning a dataset is removing any records that are missing key variables.
Then, simple statistical methods help researchers identify and remove outliers. Other information that data scientists often remove includes any time multiple columns are highly correlated. They also look for variables where the entire dataset shows near zero variance.
This data cleaning can often reduce a large dataset down to a fraction of its original size that's actually usable for machine learning.
Some simple algorithms can learn on a small dataset. However, if you have a complex problem you want to solve with machine learning, you'll need a large training dataset. There are a few reasons why this is the case.
Small data sets can work successfully for machine learning when you use a model that has low complexity. However, the more nuanced you want your results to be, the more likely you are to overfit the model to the data. Overfitting is when the model makes broad assumptions based on limited data.
It's called overfitting because the model will skew toward high, low, or otherwise outlying data points. The true answer might be somewhere closer to the middle, but since your dataset was limited, the model will bias captures the message and the noise training data. In essence, the model has learned the training data too well and has failed to get the general picture.
With more data, the model can get more accurate averages and begin to sort through the noise. That makes intuitive sense, but how do data scientists decide how much data is enough data?
Well, that answer is part statistics and part available computing resources. It also depends on the complexity of the algorithm.
When data scientists have too much data, they use something called a learning curve to plot the prediction accuracy vs. the size of the training set.
For example, the algorithm may reach 80% accuracy after 100 training samples and 90% accuracy after 200 samples. Data scientists can keep going along that curve to see where accuracy maxes out, and how many training samples they'll need to get there.
Another consideration for whether you have enough data is cross-validation. In addition to the training data, data scientists set aside some of the original datasets for testing whether the algorithm is successful. For example, one common scheme is 10-fold cross-validation.
The original dataset gets split into 10 equal groups. One group is set aside and the data scientists train the model using the remaining nine groups. Then, when model training is complete, they run the model on the data they set aside to test how accurately it performs.
Cross-validation takes more time because you have to train the models and then run them, often comparing multiple algorithms to see which performs the best. However, the extra time is worth it.
Cross-validation is essential to building a successful machine learning model since it allows researchers to identify and correct mistakes early in the process.
For unsupervised learning, all you need is a good, large dataset. From there, you can draw some conclusions about trends or clusters in the data. However, unsupervised learning applications are limited in the types of conclusions they can draw.
For most machine learning applications where you'd like to use input variables to predict an outcome, then you'll need to perform supervised learning.
Supervised learning requires a dataset that's labeled with the correct answers. A simple way to think about it is the algorithm will make a guess, and then it will use the label to check its answer.
If it gets the answer correct, the algorithm knows to increase the weight it gives to the factors that contributed to the right answer. If it gets the answer incorrect, the algorithm will decrease or otherwise adjust the weight it gives to the factors that produced the wrong answer.
Of course, the challenge is most data is not labeled. Companies and governments collect an enormous amount of data every year, but that data doesn't conveniently come along with the answers.
(If it did, there wouldn't be much use for machine learning or predictive statistics!) Before we can train a supervised learning algorithm, we need to add labels to the raw data in order to make it useful.
For instance, an algorithm might be working in computer vision and we need it to correctly identify stop signs. We may have a bunch of images, but we need to go through and label whether or not there is a stop sign in each of the images.
Labeling data can be one of the most expensive and time-consuming parts of training a machine learning algorithm. It's also a risk that poor or inaccurate labeling can introduce bias into the training dataset and compromise the whole project.
If the data doesn't already have labels, there are generally two ways we can add those labels.
Often, we use machine learning to teach computers to do tasks that we humans are intuitively good at. The stop sign example is a good one. When we see an octagonal, red shape with STOP, we know what we're looking at.
Our brains are great at understanding context. Even if we can't see the whole sign, it has graffiti on it, or it's at a weird angle, we can still identify a stop sign when we see one.
Machines can't intuitively do that.
As such, often the best way to label datasets is for humans to do it. Data scientists employ real people to look through datasets and do the job that eventually the computer will learn to do.
It could be identifying stop signs in photos, estimating distances, reading words, recognizing facial expressions, interpreting maps, or even making aesthetic or ethical judgments.
There's an argument to be made that data labeling could be the new blue-collar job of the AI era. The demand for labelers will be so large as every new ML application requires a training dataset.
Human labelers are great at these tasks. However, compared to computers, they're slow. Paying real people to label data is also expensive, prohibitively so for some use cases. As we've already covered above, humans are also biased. If a labeler or group of labelers have a bias, then that bias will likely appear in the final model.
An additional consideration is sometimes humans aren't so good at labeling. They might misjudge or jump to conclusions. As humans, we're overconfident in our own opinions, sometimes at the expense of objective truth. When we deploy machine learning in more nuanced use cases, these are all considerations we have to take into account.
All that said, humans are still the best data labelers we have. However, there are now attempts to get computers to take part in the labeling portion of machine learning as well.
Synthetic data is an emerging field in machine learning. The basic idea is to use a computer to generate labeled datasets from scratch.
Take our stop sign problem, for example. We could model a stop sign in a 3D CGI environment. Then, we could render images of that CGI stop sign in different backgrounds, angles, and lighting conditions.
The resultant dataset would have a wide amount of variation that we could control. It would already be labeled based on whether the stop sign appeared in the rendered image.
This approach is exciting because it allows us to make complex datasets very quickly. They come pre-labeled and formatted to be fed into an algorithm. We also know that the labels are objectively correct. We can measure various variables in the synthetic dataset to high precision.
Of course, there are drawbacks as well. The biggest challenge is domain transfer. These image renderings and other types of synthetic data need to have fidelity to the real world. Ultimately, the goal is for the machine learning model to work in the real world.
The fear is that, if we train it on computer-generated data, then the model may be good at recognizing rendered stop signs, but not real ones. Solving these fidelity and domain transfer problems are a major challenge for advocates of synthetic data.
Synthetic data may not necessarily be cheaper than human-labeled data, either. Creating a synthetic dataset requires a high level of expertise. Paying such experts would involve a significant investment up front.
Such an approach likely only makes sense when you need thousands of data points, as a synthetic data generation event can scale much more easily than human-labeled data.
Finally, synthetic data can't help with labels that are inherently human-based, like aesthetics or ethics. Ultimately, we'll likely end up with a combination of synthetic and human labeled data for supervised learning.
This blog could get very messy and confusing very quickly. That's because machine learning algorithms rely on complex statistics and mathematics to drive their results.
To truly understand ML algorithms you'd want to study supervised/unsupervised learning, topological data analysis, optimization methods, dimensionality reduction strategies, computational differential geometry, and differential equations.
Since this is a blog for beginners, however, and I'm by no means an expert on ML algorithms, I'll avoid the math and do my best to explain them simply.
There are entire Ph.D. programs on the subject of machine learning algorithms. You could spend years becoming an expert in this field, so there's no possible way I could explain it all in a blog anyway.
That said if the contents of this blog interest you, pursuing a Ph.D. in machine learning could pay off big time. Tech companies are scooping up PhDs and offering them salaries of $300k-$600k to write the algorithms for the newest and best machine learning applications.
I don't have a Ph.D. in machine learning, and if you're reading this blog you're likely a beginner to the concepts, anyway. So, let's take a look at the most basic functions of a machine learning algorithm, without getting into the math.
Inputs - All algorithms need some sort of input data. In data science applications that could be as little as a single variable. More likely, however, the model will be learning the relationship between tens, hundreds, or even thousands of variables at any time.
For more complex applications, like computer vision, we need ways to turn visual information into variables that the computer can understand. There are different approaches depending on the context and problem you're trying to solve.
Needless to say, even inputting data to an algorithm can be complicated, before the machine even does any learning. Choosing or creating an algorithm is highly dependent on the data you have to feed it and the context.
Output vectors - At the end of any machine learning project, you want some type of output. However, it's not always clear exactly what data you'll need to satisfy your project. Choosing output vectors can be more complicated than it seems at first.
Of course, for many projects, the output will be obvious depending on your objectives. Nevertheless, as machine learning enters areas that are more nuanced and ambiguous, choosing and coordinating outputs can be a task in itself. You can't choose the right algorithm for your project if you don't have a clear idea of your expected outcome.
Adjustment - Machine learning algorithms use feedback loops to fit a model to the data. This can happen in different ways. Sometimes an algorithm will try a random combination of factors until one begins to work, and that combination will receive a higher weight in future training tests.
Other times, the algorithm has a method built in for finding and fitting a trend in the data that gradually tune over time.
This is where data scientists have to be careful. Sometimes an algorithm learns to fit its training data too well. That is to say, the model has become too specific to the data that it was trained on and no longer predicts general trends or classifications in the real world.
In essence, the algorithm has learned its training data too well. This is called "overfitting" and it's an important concept to understand in machine learning. When data scientists train models, they have to make sure that their models walk a fine line between making specific predictions and being accurate generally.
Data scientists spend a lot of time thinking about and adjusting their algorithms to mitigate overfitting. However, they also test multiple algorithms at once side-by-side to see which ones perform best after training.
A key part of choosing or writing an algorithm is understanding how the algorithm adjusts over time in response to training data. These feedback loops are often where the complex mathematics come into play to help the algorithm decide which factors contributed to its success and should, therefore, be more heavily weighted.
They also help the algorithm determine how much to increase or decrease the weight of a contributing factor.
Okay, so we've covered a general overview of how an algorithm functions. Let's look at some of the most popular ones to get more specific details about how each one works.
This is a simple algorithm that relies on concepts taught in most Statistics 101 classes. Linear regression is the challenge of fitting a straight line to a set of points. This line tries to predict the overall trend for a dataset and you can use the line to make a probability prediction for new data points.
There are multiple approaches to linear regression, but each at its core is focused on finding the equation of a straight line that fits the training data. As you add more training data, the line adjusts to minimize the distance from all data points. As such, linear regression works best on very big datasets.
This is a fairly simple type of algorithm, but one of the key maxims of machine learning is don't use a complex algorithm where a simple one works just as well.
If linear regression was a straight line on a 2D plane, logistic regression is its older brother that uses curvy lines on a multi-dimensional area. It's much more powerful than linear regression, but it's also more complex.
Logistic regression can handle more than one explanatory variable. It is a classification algorithm, and its outputs are binary (a scale from 0 to 1). As a result, it models the probability (e.g. ".887" or ".051") that the input is part of a given classification.
If you apply it to several classifications, you'll get the probability of the data point belonging to each class. Mapping these probabilities gives you a non-linear multi-planar curve known as a "sigmoid." Logistic regression the simplest algorithm for non-linear applications.
If you've seen a flowchart, then you understand the basic idea behind a decision tree. The tree sets out a set of criteria, if the first criteria is a "yes" then the algorithm moves along the tree to the yes direction.
If it's a "no," the algorithm moves in the other direction. Decision tree algorithms fine-tune the criteria and possible answers until they give a good answer consistently.
In modern machine learning, it's rare to see a single decision tree. Instead, they often get incorporated with other trees simultaneously to build efficient decision-making algorithms.
Random forest is one type of algorithm that combines multiple decision trees. It introduces the concept of a "weak learner" to the algorithm. Basically, a weak learner is a predictor that does poorly on its own, but when used in concert with other weak learners, the wisdom of crowds produces a good result.
Randomly implemented decision trees are the weak learners in a random forest. Each decision tree learns as part of the algorithm implementation. However, an overarching strong predictor is also learning how to combine the results from the various trees.
This is an unsupervised learning algorithm that tries to group the data into k number of clusters. Although it's unsupervised, the data scientist does need to provide direction at the start.
They'll set images or data points that should be the center of each cluster. In other words, data points that are archetypal of what the cluster represents. Over the course of training, all the images or data points get associated with the cluster they're nearest to. Eventually, these data points converge with their appropriate clusters.
There are other faster or more optimized methods for unsupervised clustering. However, K-means remains popular because it's well established, documented, and generally effective.
K-Nearest Neighbors (KNN) is a classification algorithm. It shares some similarities with K-Means Clustering, but it is fundamentally different because it's a supervised learning algorithm while K-Means is unsupervised.
Hence the slight difference in terminology from clustering to classification. KNN gets trained using labeled data so that it can label future data. K-Means can only attempt to group data points together.
KNN compares new data points to the existing data points from the labeled training data set. It then looks for the "closest neighbors" to that new data and associates those labels.
Principal Components Analysis (PCA) reduces a dataset down to its main trends. It's an unsupervised algorithm that you would use on a very large dataset to understand the data in simpler terms.
It reduces the dimensions of your data. However, it also focuses on the great variance between the dimensions (or principal components) so you don't lose the original dataset's behavior.
We've covered a few of the major algorithms and there are several more that make up the core of machine learning theory. Beyond these core algorithms, however, it's rare for someone to invent something truly new. Typically, new algorithms are improvements upon existing theories. Or, they customize an algorithm for use in a new scenario.
Part of the reason that new algorithms rarely get invented is that it's really hard. Creating an algorithm requires a strong grasp of complex mathematics. It also requires extensive proofs and testing. Additionally, the low hanging and obvious algorithms have already been invented.
But that's not all. Good algorithms are both effective and efficient, a tricky combination to nail down. Machine learning is a computational problem with thousands of data points as much as it's a mathematics problem.
Debugging algorithms can also be very difficult since it's not straightforward where things have gone wrong.
Whenever possible, a machine learning project should apply existing tested and reviewed algorithms. Coding your own algorithms from scratch or cobbling together a hybrid approach is frowned upon because it can introduce errors, slow results, or be buggy.
Sometimes, developers and data scientists will need to tweak or implement an existing algorithm in a new context. Or maybe an existing algorithm isn't fast enough for the desired application. However, most machine learning applications can use existing algorithms and libraries effectively without having to code from scratch.
This is the step where the actual machine learning happens. After preparing the dataset, data scientists select several similar algorithms they think might work to accomplish the task at hand. Now, the challenge is to train those algorithms on the dataset and compare the results.
Often, it can be hard to tell which algorithm will work the best for a machine learning application before starting. For that reason, the best practice is to train multiple algorithms at first, select one or a few that perform the best, and then tune those algorithms until you get one model that works best for your needs.
When we say "best" that could mean multiple things. Of course, we want the model to make accurate predictions, so accuracy is an important component.
However, if the model is resource or time intensive to get those results, then it may make more sense to choose a simpler algorithm. We'll get slightly less accurate results, but they'll come much more quickly.
Machine learning sits at the intersection of statistics, calculus, and computer science. Since we're dealing with machines we're going to naturally need to write machine learning instructions in a programming language. With the growth in interest in ML, it's quickly becoming an enormous area of growth for new software developers.
Skills in machine learning are highly valuable So far, we haven't talked about the programming languages and approaches that developers use to code and create their machine learning applications. This section will be just a brief overview of the major players.
Python is by far the most popular language for creating machine learning applications. It's also the most preferred language in developer surveys about machine learning. A large part of Python's success is its simplicity compared to other programming languages.
In addition, Google's open source library of machine learning algorithms, TensorFlow, is Python-based. The resources and the community are strong for machine learning applications built on Python.
Java and C/C++ follow Python by a wide margin in popularity. They're older languages, and they allow for lower level optimization of the environment where the algorithm will be running. Java and C/C++ get used in a lot of applications, not just machine learning.
This means there are a lot of developers out there who understand these languages. There are some machine learning libraries for these languages, though nothing on the scale of TensorFlow.
R is another programming language that often enters the machine learning conversation. It's a specialized language that's designed for data science applications.
While R certainly has its place in machine learning, it's rare for a project to choose R as its main or preferred language. Instead, it's more of a complementary language to the ones listed above.
Of course, it's possible to write machine learning code in many different languages. There are other languages that specialize in certain areas of statistics, data science, or modeling.
Julia, Scala, Ruby, Octave, MATLAB, and SAS are all options that occasionally arise in machine learning projects. However, these languages are the exceptions rather than the rule.
Once you've picked a programming language and installed a library to help you implement the algorithms you want to run, you're ready to start training your algorithms.
There are two types of machine learning training. The first is static training that receives training offline and is then finished learning until data scientists initiate a new training session. The second is a dynamic training where the model continues to learn in production, indefinitely.
Static models are much easier to build. They're also easier to test for accuracy and tend to run into fewer issues in deployment. If your data isn't changing over time or is changing very slowly, a static model is a way to go since it's cheaper and easier to maintain.
Dynamic models are much more work-intensive to implement. They also require constant monitoring of the incoming data to make sure it doesn't inappropriately skew the model. Since dynamic models adapt to changing data, they're much better at predicting things like markets or weather, where patterns are constantly in flux.
A data scientist's job doesn't stop with picking a handful of algorithms and letting them run. In order to get optimal performance the person programming the algorithm has to set the input parameters that will go into the algorithm.
Since machine learning problems are often complex, it can be difficult to decide which parameters are relevant and how many to include.
Trying different combinations of parameters and refining the best mix is known as algorithm tuning. There is no absolute right answer here. Instead, each tuning job is a matter of matching the algorithm to the context it's being deployed in.
Another related concept to tuning is feature engineering. Sometimes, like in the case of image recognition, feeding a computer a stream of data isn't enough for it to make sense of what it's seeing.
While deep learning and neural networks have made progress on the front of computers learning from images, feature engineering is a handy way to tell a computer what to look for.
You might engineer a feature that helps a computer identify a straight line or the edge of an object. Since you manually coded that feature, it's not technically machine learning, but now the machine knows what to look for.
Engineering features can dramatically increase performance.
If everything goes well, then the result is a model that has learned to accurately make predictions, clusters, or classifications in your data.
However, the dark side of machine learning is algorithms that don't work. There is a lot of time and money going toward machine learning applications right now. Unfortunately, many of these applications will end up as duds.
Perhaps the algorithms were poorly chosen or implemented. More likely, the project doesn't have enough or the right type of data to be successful. It's underreported how often machine learning projects fail.
The frustrating thing is it can be difficult to tell why your project is failing. You could have tons of data and test and tune many algorithms to no avail. This is especially true with complex problems or algorithms that implement multi-layered neural networks or random forests.
It's difficult to say where things went wrong. Sometimes, data scientists invest a lot of time in a project, only to find they need to throw everything away and start over with more, new, or different data.
This may seem like a strange section to include in a blog that's so optimistic about machine learning. However, I think it's important to highlight the fact that there's still a lot we don't know about creating and using machine learning projects. Projects fail all the time and fixing them is hard.
That's an important reality of machine learning. It's critical that we acknowledge that just because a machine learning model produces an answer doesn't mean it's always right or incontrovertible.
We should respect and admire machine learning as a tool. But in the end, it's just that: a tool.
Now that you've got a basic understanding of how machine learning works, it's interesting to take a look at everyday examples of machine learning that you may not have even recognized.
When you open Google Maps to get directions, you're using a dynamic machine-learning model. It uses anonymized cell phone data from drivers in your area to get commute times for various paths.
The model also integrates data from Waze about road closures, accidents, and other user reports. Together, the model predicts the fastest route and estimated arrival time based on real-time information.
Lyft and Uber build on top of this data with their own machine learning algorithms that drive dynamic pricing and fare calculation. They also let you know how soon to expect a driver and when you're likely to arrive at your destination, even accounting for picking up and routing other people in the case of Uber Pool or Lyft Line ridesharing options.
These same routing, logistics, and arrival calculations also apply in long-distance trucking, shipping, and even airplane navigation. Models help predict the fastest, safest way to transport goods and people while maximizing efficiency.
Basically any time a company makes a recommendation to you online, you can assume a machine learning algorithm helped make that prediction. Amazon knows what products you might be interested in based on what you've looked at and purchased before. Netflix knows what movies you'd enjoy because it learns from all the movies you've watched before.
This goes deeper than just serving custom recommendations, it also applies to advertising. Facebook knows a ton of personal data about you, and they're using that data to customize which advertisements they show you. The same can be said for YouTube, Twitter, Instagram, and all other social media.
Additionally, Google uses your personal information to customize the results you receive when conducting a search. For instance, it is more likely to recommend local businesses in your town or articles from websites or writers that you've previously visited.
Similar to social media, Google is also customizing its ads for you. Don't believe me? Conduct a search on Google in your browser and then do the same search in an incognito window in your browser (gets rid of cookies and login information). For most searches, especially topics you've researched before, you'll see you're getting different results.
Even in-person machine learning will change the way we buy products. Major retailers are looking at computer vision applications that identify what you've already got in your basket and can make recommendations.
Other systems are using facial recognition to identify when customers are lost or confused, and they can notify an employee to help. These systems are still in their infancy, but they represent the ways machine learning is integrating with every aspect of life, including human-to-human interactions.
Every major bank is using machine learning to help simplify their operations. In regulatory technology, machine learning algorithms can help banks identify if their processes and documentation are compliant to government standards. Other machine learning algorithms predict market trends or provide investment insights.
For loan applications or credit lines, machine learning can help banks predict the risk of lending to a given customer. These models can then suggest terms and rates that are individualized to the applicant.
In banking, ML-powered character recognition makes it possible to deposit a check using your smartphone's camera. Machine learning can also detect and prevent fraudulent transactions from clearing on your account.
The likes of Siri and Alexa rely on machine learning to understand and respond to human speech. Conversational AI is the cutting edge of machine learning and neural network training.
We've gotten pretty good at speech recognition and answering basic questions like "What will the weather be today?" The next challenge is getting a conversational AI that can talk about music, literature, current events, or other complex ideas.
The role of voice will only continue to expand over the coming years as we come to increasingly rely on our personal assistants. This is especially powerful when combined with the movement toward smart homes and autonomous vehicles.
It's possible to imagine a future where you can control every aspect of your home and transportation intuitively by speaking with a voice assistant. In turn, each of these systems--like smart thermostats, intelligent security systems, and autonomous cars--use their own machine learning algorithms to perform the tasks we require of them.
Of course, there are tons of other use cases for machine learning in healthcare, manufacturing, agriculture, and everywhere else in our lives. Machine learning is useful anywhere there's data and we need help understanding, predicting, or using that data.
Machine learning is powerful, and it will continue to gain prominence in our daily lives. As such, it's important that everyone has a basic understanding of how it works, the potential flaws, and enormous opportunities. Hopefully, this quick beginner's guide has provided a solid foundation for the layman interested in the basics.
That said, there's so much more to machine learning that isn't covered in this blog! There are great resources available online and in print to expand your knowledge of this important technology even further. It's my hope that this is only the beginning of your machine learning journey.


Carlisle Brass: Can you Handle it?
Carlisle Brass, a division of the highly-successful Carlisle Design Group, has been producing a superlative range of brassware for more than 25 years, during which time it has developed into the leading distributor of door furniture in the UK. We are proud suppliers of a large selection of Carlisle Brass products, including door furniture, hinges, cabinet hardware, letter plates and window fittings.
ISO-registered Carlisle Brass, like its parent company, has a strong focus on innovation and employs a talented, hand-picked group of researchers, designers and engineers. This group continually create new products and optimise those already in existence, allowing the company to build and maintain its impressive reputation. It is a notable fact that nine out of ten of the top housebuilders in the UK recommend Carlisle Design Group and specify more than 12,000 products from the range as industry standard.
Combining strength and durability with aesthetic appeal, Carlisle Brass door handles are incredibly popular. Available in an eclectic range of styles and finishes with customisable options, these handles can suit a wide variety of applications and are guaranteed to provide the most professional results every time.
A popular choice is the DDA-compliant Satin Lever Door Handle on Round Rose. It is supplied with all the necessary bolt-through fixings and features square spindle operation. The return-to-door handles should be used in conjunction only with Eurospec locks and latches, of which you can purchase many varieties here at JAS Timber.
Eurospec Architectural Ironmongery are members of the Guild of Architectural Ironmongers and the Master Locksmith's Association. The brand has achieved ISO 14001 and 19001 accreditation and, with more than 25 years of practical experience, is now an industry-leader in the field - 9 out of 10 premier housebuilders specify Eurospec products as standard. Eurospec focus sharply on sustainability and environmental awareness, ensuring that its products and practices comply strictly with relevant standards.
Find out more by checking out the products page.


The Lakehouse is a custom-built vacation rental located between Pentwater and Silver Lake, only 1 lot off of Lake Michigan – enjoy leisurely days and beautiful sunsets on 750ft of semi-private beachfront access. Walk for miles in either direction!
The home has everything you need for a relaxing vacation (except linens & towels, please bring your own). Dining space for 12 people, grill, picnic table, and both open and enclosed porch space for excellent and bug-free meals. Air conditioning, vaulted ceilings, and hardwood floors make for a comfortable home-away-from-home that feels like a cottage retreat!
The location between, but outside of, both Pentwater and Silver Lake means quiet seclusion from the bustle of tourists, but still convenient for exploring both popular destinations. Shop and dine in Pentwater, explore the dunes and cruise go-karts in Silver Lake – a compromise perfect for kids and adults alike.
* Please no smoking or pets allowed. Children welcome.
* Please bring your own linens and towels.


ps. : in the picture I think that it is still a bit bloated or ??
reply - 2: First of all, congratulations!
My dwarf should come on 12.12.
I suffer from extreme nausea and therefore have lost 5.5kg so far! Increase = 0 !! Unfortunately, nothing helps me and I just try if I have an appetite to eat what I want! I prefer to drink tap water or sometimes a stale (yes, really real) coke!


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The rise and fall of a top craft brewery, as told by the comments section
Exploring the industry's frothy recent past via Modern Times Beer's 2019 crowdfunding site
Source: Modern Times (edited)
Times are tough for Modern Times Beer. The one-time juggernaut hailing from San Diego's venerable craft brewing scene expanded like gangbusters over the past decade, fueled by industry tailwinds and a whole lotta debt. Then came Covid; then came the resignation of founder and CEO Jacob McKean in the face of allegations of a toxic, nepotistic workplace; then came the closure of four of the brewery's eight taprooms. This Friday, Modern Times will go up for auction to the highest bidder, and the stalking horse offer from fellow Pacific-side player Maui Brewing Company is a scant $7.62 million.
To the average observer, the firm's fall may recall Icarus's sun-scorched plunge. Industry insiders have a more modern cautionary tale to reference: Ballast Point. The House That Grapefruit Scuplin Built sold for a cool billion to Constellation Brands in 2015, the height of the American craft beer business's salad days, only to be hocked in 2019 for dimes on the dollar (or was it nickels?) For many in the beer business, Ballast Point became a parable about overvaluing craft breweries—but not, apparently, for Modern Times.
Michael Kiser @mpkiser
Old enough to remember them using the Ballast Point $1B valuation (and adding more on top) as the basis for a crowd funding campaign that seemed wildly exploitative.
Kate Bernot @kbernot
$7.6 million. https://t.co/gl8wzHzDUN https://t.co/5sxyFyQWfr
12:17 PM ∙ Jun 1, 2022
In 2019, the brewery launched a campaign on the Wefunder crowdfunding platform, offering rank-and-file drinkers the opportunity to invest in its then-fast-growing operation. The fundraise implied a valuation of $264 million, a figure that Modern Times arrived at using a… ah… dubious benchmark. Reported Brewbound at the time (emphasis mine):
According to the Wefunder documents, Modern Times arrived at its $264 million valuation by using a similar multiplier ($3,448 per barrel) to the one used for Constellation Brands' $1 billion purchase of a much larger Ballast Point Brewing Company in 2015.
That's right: four years after Constellation's billion-dollar buy-out of Ballast Point—during which course the conglomerate took a pair of impairments on the craft brewer's trademarks, tacitly admitting that they'd overpaid for it—Modern Times was pitching the drinking public on shares pegged to that boondoggle's bananas price-per-barrel. Not great, particularly considering that in the intervening time, the number of craft breweries leapt from 4,847 to 8,530, and a little something called hard seltzer hit its mainstream stride. That's a shitload of new competition with a massive shift in customer preference on top! But even as tailwinds turned to headwinds, Modern Times turned to the industry's frothiest watermark on record for a valuation—then "formally invited" unaccredited would-be investors "to the party" in a pitch video.
The U.S. brewery count almost doubled last decade. | Source: Brewers Association (edited)
To be clear, I'm not alleging that Modern Times improperly disclosed the risks of investment in its equity offering, which, by the way, would go on to raise $1,224,431 in 2019 dollars from 1,189 investors, according to the still-live Wefunder page. (That works out to an average per-person investment of over $1,000.) But I do wonder whether the average Modern Times fan—at least some of whom were likely unaware of Constellation's Ballast discharge and broader industry trends the time—really absorbed those risks. Or, I did wonder… until I found the comments.

redpajama_set_name: RedPajamaCommonCrawl


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DeSantis Releases "Top Gun" Parody Ad
DeSantis New Top Gov Ad Is EPIC
(RepublicanNews.org) – As primary elections wind down, campaigns are starting to heat up across the country ahead of November midterms. On August 22, Governor Ron DeSantis (R-FL) released a unique ad using elements from the popular movie "Top Gun: Maverick." The video starts off at what looks like a hanger named "Freedom Headquarters," panning away to jets flying in the air.
DeSantis appears in the clip at the head of what would be a class of fighter pilots, donning a bomber jacket and classic Maverick sunglasses. Throughout the ad, he outlines the "rules of engagement" for dealing with corporate media, which include never backing down, sticking to your guns, and never "accept[ing] their narrative."
As he uses his pointer and a whiteboard to explain these rules, the video jumps back and forth to show clips of the governor effectively dealing with the media throughout his time in office. The campaign advertisement ends with his son appearing in the cockpit of a jet, saying "Let's turn and burn!" — a Tom Cruise quote from the "Top Gun: Maverick" movie.
The Florida leader's wife, Casey DeSantis, came up with the concept for the media-challenging promotion. The governor will face Representative Charlie Crist (D-FL) in the fight for the governor's mansion this fall.


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Peaky Blinders star Charlotte Riley says everything is up in the air for her character May Carleton in series five
Sarah DeenTuesday 20 Mar 2018 9:57 pm
Charlotte Riley opens up on her character in Peaky (Picture: Backgrid, BBC)
Peaky Blinders star Charlotte Riley has told Metro.co.uk everything is up in the air for her character May Carleton in series five.
Speaking to us at the RTS Awards on Tuesday evening, Charlotte last made a flying visit to see Tommy Shelby before being packed off home on a boat to keep her out of harm's way.
The Shelbys will move into the political arena in the new series, but Charlotte Isn't sure where that will leave horse trainer and Tommy's love interest May.
When we asked Charlotte about what the future holds for her character at the Royal Television Society Awards, she told us: 'I don't know. I know nothing. We're the last ones to find out.'
Peaky will return next year (Picture: Mandabach TV – Photographer: Robert Viglasky)
Previously, Peakys star Paul Anderson, who plays Arthur Shelby, teased that fans can expect to see a more manipulative Shelby family in series five.
He told Metro.co.uk earlier this year: 'What it gives Arthur and his family is more power. Tommy is in a position of power, and he can only get stronger in that position.
'So we'll all have more cover, we'll have more opportunity, and more protection. And by protection I mean by society and the police and so on.
More: Peaky Blinders
Fair City and Peaky Blinders actor Karl Shiels dies suddenly aged 47
Peaky Blinders season 5 to launch with Birmingham premiere - and you're invited
Peaky Blinders season 5 sneak peek unveils Thomas Shelby as an MP
'We'll be able to get away with things under the guise of, "we're politicians, we're respectable". But we'll be able to manipulate things more. Arthur will be even more Arthur. So it will be a lot of fun.'
Last year it was announced Peaky Blinders will return in 2019 for a fifth season.
MORE: Richard E. Grant sports 'Peaky Blinders cover-up' after being hospitalised with head wound
When does Peaky Blinders season 5 start on BBC One and who is in the cast?
Peaky Blinders actor Karl Shiels 'died in his sleep' after worrying cast mates by 'repeatedly falling asleep on set'


"UBI is a starter motor. When companies leave us they should be able to run themselves," says Jennie Ekbeck, CEO of UBI since 2012.
"We have finally reached the incubator phase that I have been looking forward to for so long", said Jennie Ekbeck, CEO of Umeå Biotech Incubator, who has an extensive background in biomedicine and as an entrepreneur.
For years Ekbeck has had ambitious plans to build the sort of business incubator she herself would have wanted to exist when she first started out. Together with her team she has systematically developed and grown UBI, which is now recognised as one of Europe's leading life science incubators.
"The life science companies that we have worked with now attract the kind of funding needed to power growth and our ambition is to help them achieve their development goals", said Ekbeck.
Today UBI is working closely with a women's network and regular regular "house meetings" at the business incubator, where companies can present their activities to one another. Over the next few years UBI will develop even more intimate event formats where established companies and startups can meet each other as well as academics and representatives from the County Council. The focus will be on the kind of themes and content that are being sought after by the industry.
"I'm look forward to more great results from UBI in the years to come. With the help of creative ideas from the university and robust business support, real social benefits can be created" he said.
Life science is a term used for the interdisciplinary focus on the development of practical applications, primarily within biomedicine: an area Umeå University has long distinguished itself in. Since 2004 UBI has supported life science companies in developing their businesses.


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Barcelona boss coy over risk of losing Luis Suarez for Atletico Madrid ahead of Las Palmas trip
Manager also provided a positive update on Gerard Pique's fitness ahead of a crucial week for the Catalans.
By Eduardo Fernandez-Abascal
February 28, 2018 15:36 GMT
Ernesto Valverde refused to confirm whether Luis Suarez or Lionel Messi could be rested against Las Palmas. Getty
Ernesto Valverde has refused to confirm whether he will rest Luis Suarez when Barcelona visit Las Palmas on Thursday (1 March) in order to avoid the risk of losing him for the weekend's clash with La Liga second-place Atletico Madrid.
Suarez has already been booked four times in La Liga this season and he appeared to try to pick up another yellow card during the 6-1 victory over Girona to ensure he had a clean slate for the clash with Diego Simeone's side.
Munir discusses potential Barcelona return but content with Alaves stay
However, the referee refrained from cautioning him and the former Liverpool star will thereore face Las Palmas knowing that another booking will see him miss the crucial visit of Atletico.
The Barcelona boss may consider giving his striker a rest on Thursday but asked about his plans during the press conference ahead of the game, Valverde just said: "We'll see. Do you want me to anticipate [the line-up]? We will see tomorrow. Each thing at it's time."
Valverde also provided a similar response when questioned whether the trip to the relegation-threatened could be a good opportunity to rest Messi and have him fresh for the game with Atletico.
"We'll see tomorrow. We have not played since on Saturday and have had a margin [to recover]. I do not want to look at coming games because this three points are very important for us to keep the [seven-point] gap [with Atletico].
"They are three points available against Atletico which are no more valuable than those against Las Palmas. There is no value in concentrating on Sunday's game if we do not win tomorrow.
The fitness of Gerard Pique has also been a concern among Barcelona fans since the centre-back picked up a knee injury during the derby against Espanyol on 4 February.
Valverde admitted following the recent Champions League clash with Chelsea that the Barcelona defender was still struggling with the issue and on Thursday (22 February) he was unable to train with the rest of his teammates.
Pique still made Valverde's starting line-up against Girona but in the second half the defender set alarms bell ringing again after being replaced by Thomas Vermaelen.
The Barca boss has now played down those concerns, claiming that Pique's substitution in the Catalan derby had nothing to do with his previous injury.
"He is fine. Yesterday [on Tuesday] he trained well. If he is playing, it is because he is fit. My idea was not to change him [against Girona] but he suffered a knock. But it has nothing to do with the injury that [he suffered] against Espanyol. There is nothing strange," Valverde assured.
Ousmane Dembele has also appeared to put his hamstring woes behind him after the France starlet completed the full 90 minutes during the win over Girona.
Valverde was positive about Dembele's performance but warned that he will still need to some time to adapt to become a regular starter at Barcelona.
"Emotionally I see him well. He trains well. Then the competition marks whether a player has more game time or not. There is a lot of competition in our club. Dembele played the whole game the other day. He has to try to be decisive in the attack and has to comply with the defensive obligations. He is taking steps little by little," the Barcelona boss added.
Related topics : Luis suarez Barcelona FC Lionel Messi


I have covered quite a few of the really serious side effects that I experienced. Now comes one that is a little harder to admit to having experienced. The funny thing is, I know I am not the only one who has had a similar issue! I won't out that person, but I will tell you about me. I will admit, that prior to being diagnosed, I would dabble in a little Kardashian viewing, and That Guy and I were pretty into Big Brother during the summer as a standing tv date. That was my end of reality television though. I did not understand any of the Real Housewives shows, and would out and out tease a friend who watched them and talked to me like I had any idea what in the hell she was talking about. I will admit to watching a marathon of Bethany's married or having a baby show or something once when I had a stomach virus. I never got into American Idol, Dancing with the Stars, or The Bachelor(ette) series. I was the girl who volunteered to go save seats at the bar during the Survivor finale in college. So very much not my thing is what I am getting at here.
Well, that is so very far from the truth now. I won't list the entire sad list of shows I enjoy, but let's just say I am well versed in the RHOAE (Almost Everywhere). I catch the Kardashian drama when I am able. And then there are the spinoffs. SUR! Talk about guilty pleasure show. A steady rotation between E! and Bravo was a big part of my treatment. I wish I were joking. Actually, let's be honest, I don't.
These shows were my escape. They were so not my real life. And the lack of similarity to the life I live had nothing to do with going through cancer treatment. I did have a hard time some nights watching the ridiculous superficial consumption lifestyles when I was going through a fight for my life. I mean, obviously. More often though, I enjoyed not thinking about life or death. I enjoyed looking at the beautiful places the people traveled to, since I was stuck in the same place. I was able to turn off my brain and relax without the anxiety of my never ending thought process of what if's.
And yes I do still watch many of the Real Housewives series to the point that my mom finally has her chance to tease me a bit!


The first three Emotional FItness Skills take you on the path to the fourth skill. That one? Thinking before acting. Not as easy as you think.
WHY THINKING IS SO HARD TO DO: The Freudians and the Behaviorist are two of the best known schools trying to figure out why people behave the way they do. They are miles apart in most things. Not on this. Both agree on the power of pleasure over pain.
Even that idea bears a bit of thought for some people seem to like pain. Here are two reasons why.
First: people seem to like not the pain but the high that appears when pain vanishes. Think of the pain of fear when you think you are headed for a horrible automobile crash and the high of the relief when you don't crash. Think of the even higher relief when you crash but are not hurt.
This explains much risk taking behavior, but also why some stay in abusive relationships.
Second: The need for certainty. Cognitive theorist Jerome Kagan is the leading authority on this. He notes that infants and children in their first efforts to understand the world accept what is as normal and what should be. Abused children think abuse is normal.
Kagan makes the point that we like change, but only small changes. Changes that shake our world view shake us in painful ways.
As a foster parent, I saw how too much change drove some of our foster children to act out. Living with new parents meant living with change.
Most threatened were the foster children in our care who had been seriously abused. More than having to cope with a new environment, living with parents who didn't abuse was the scariest change for these foster children. Why? Living with us created uncertainty about their parents love.
The physical pain was more bearable than the thought that parents were not supposed to be abusive.
A number of our foster children had been adopted as infants. Until they reached their teens, they accepted what their adoptive parents said. That was usually, something about their being loved enough to be given away. Hard for some of us to swallow but until puberty set in, most adopted children did not question the truthful of this. Then for some moving into their teen years, meant being able to think more deeply. As with John, adopted children did not want to think that maybe their birth parents just didn't care.
Complicated? You bet. Which is why you need to learn to think harder before acting as a feeling urges you to act.
The first thing to think about is if you need to act. Feelings want you to act and often sound a 911 siren telling you to act quickly without thinking. Not helpful. This Poster Coach gives you some ideas to think about before acting.
In order to think wisely we need to know and name the feeling visiting us, then we need to stop and self-sooth. That is what the previous sessions have been focused on. If you haven't started the course at the beginning, do so now. Here is session one followed by the others.
Most parents do not understand how children think. I did not for many years, and I am supposed to know those things. I am surprised at how many Parent Advisors and therapist still don't tailor their suggestions to the child's thinking age and stage. So here is a link to one of my posts about How Children Think.
Learning when to act immediately on a feeling's urging. Actually never, but in some situations you need to think only for a few seconds before acting.
I often use these prompts to spark my posts. They work to improve critical thinking which is the heart of emotional intelligence. Critical thinking is about thinking more deeply and ruling out some common thinking erros.
You can think about the prompts as they are stated or use them to spark other thoughts which is what I usually do. If I put on my thinking cap the prompts can be related to Emotional Fitness. Here's how I did that for this post.
How this relates to emotional fitness and today's post: Easy because unless we have all become machines, people will still need at least one or two of my exercises. So they will find a list of my EFTI books including this 101 work course in its final form as a work book. Hopefully, there will also be a working link all other EFTI material.


text:
Q: Measure I2C bus capacitance We are trying to calculate how many I2C slaves we can hook up the I2C bus.
We tried to run a square wave through a serial resistor and measure the rise time but we get a strange result. More nodes decrease the capacitance, not increasing it as expected.
Is there another way to get the capacitance?

A: The answer is "it depends." It depends on how strongly each device can drive the I2C lines; on how small of pull-up resistors all can tolerate; on what speed the I2C is clocking at; and how much parasitic capacitance is induced by the bus wiring length and topology. Smaller pull-ups, slower clock, and shorter (external-PCB, good spacing) bus distances all reduce capacitance and thus allow more devices. Even prototyping on a solderless breadboard is not ideal, as the pin connections will likely introduce significantly more capacitance than say, a 2-sided PCB will. All of these aspects could be calculated and/or simulated, but Renesas has a good point in that "testing it" may be the fastest and most accurate way to arrive at a real-world answer.

redpajama_set_name: RedPajamaStackExchange


Forged 6.1L engine, bored, balanced, and blueprinted with a custom Comp Cam fitted with a Kenne Bell 2.6 supercharger kit and exhaust featuring American Racing headers, and a Magnaflow cat-back exhaust. All tuned by DiabloSport.
Not only will it handle good, but it's puts down 630 horses to the rear wheels. Plus it looks bitchin'!
Some publicity for a local area performance shop!


text:
Q: Is IntentService an implementation of Command Pattern? According to Wikipedia:

In object-oriented programming, the command pattern is a behavioral design pattern in which an object is used to encapsulate all information needed to perform an action or trigger an event at a later time. This information includes the method name, the object that owns the method and values for the method parameters.

And according to professor Schmidt's text, a command has:

*

*Time-independent execution of application logic. The encapsulation of
application logic allows to queue it and execute it at a different point in
time.

*Context-independent execution of the application logic. The separation
between application logic and context allows to execute the application
in separate contexts, such as in a different thread or using a different state.

*Exchangeability of application logic. The separation between application
logic and context allows to easier exchange the application logic.

If you encapsulate all information into an Intent, the onHandleIntent works as an abstract method to the command executor, just like described at command processor pattern text.
Thus, instead of explicitly implement the executor in order to invoke a command, you simply delegate command execution to the operating system.
So the questions are:

*

*Is IntentService the framework implementation for command pattern?

*In affirmative case, why some Android MVP implementations explicitly implements its own, using ThreadExecutor as consumer and Runnable as commands, instead of using the one provided by the framework?

A: I would argue IntentService is not a framework implementation of the command-executor design pattern, because it does not meet the requirement of the separation of application logic and application context.
The command-executor pattern is like providing a button on a remote control to replay some action(s) on-demand (dim the lights, close curtains, turn on Barry Manilow) no matter who presses it in whatever context. Command-executor is analogous to materialising a personal assistant who then does a bunch of predetermined tasks for you on-demand.
On the other hand, the use of the IntentService class is analogous to a pedestrian hailing a taxi cab or a pub patron flagging the bartender: The agent requests a service (expresses an intent), the agent enters a queue to receive the service, the agent finally receives the service. Unlike the command-executor, this sequence requires the participation of the agent, which itself requires that the agent be in the right kind of context to (want to) receive the service. But this is not what the command-executor pattern prescribes. Indeed, the API class overview for IntentService in the link you provided suggests that the IntentService class fits another established design pattern:

This "work queue processor" pattern is commonly used to offload tasks
  from an application's main thread.  The IntentService class exists to
  simplify this pattern and take care of the mechanics.  To use it,
  extend  IntentService and implement onHandleIntent(Intent).
  IntentService  will receive the Intents, launch a worker thread, and
  stop the service as  appropriate.

In a sense, the IntentService could be considered a command-executor if only the subject of the service could also be included in the abstraction. So for our analogies, if at any time we wished, we could invoke a command to "send my niece Suzy the birthday present I bought her" or "send me to Beijing to complete the business merger", then IntentService might be a command-executor. But when we state the comparison this way, it becomes clear IntentService could not be command-executor, because these events are dependent on context in the sense that they need objects or agents' participations prior to being able to run. This is not true for command-executors, which package all the necessary components for ready replay at any time.


Are Mobile Phones Set to Replace Computers?
Following on from my post about the first 3D mobile phone I have found myself this week looking more into mobile phone market and its future potential. At all the trade shows I have been to recently I have noticed a significant increase in the amount of organisations offering innovative solutions for mobile phones and devices than ever before. At the Broadcast Video Expo at Earls Court last week I noticed a lot of companies offering streaming solutions for smartphones.
The demos were really impressive so I decided to have a look into the market to see if a dedicated phone streaming offering would be worthwhile to our customers. We all know that most people have a mobile phone these days but it is less clear as to how they will use these devices in the future. Will people use them as a replacement for their computers in terms of work and play? Will applications put an end to some high street outlets as people choose to do their shopping and banking via their phones? And from our company's perspective, will organisations and individuals choose to send and receive video content via mobile phones instead of conventional outlets?
The first mobile phones were launched in the early 1980's and were the size of a microwave, heavier than a bag of cement and could only be afforded by yuppies driving Electric Blue Lamborghinis. Fast forward 30 years the technological development has been staggering. Mobile phones have fundamentally changed the way an entire generation of people across the world communicate and share and consume information. I have had a look at a few articles and blog posts and it appears that our mobiles are going to be more important to us than ever in the coming years and companies like ours need to recognise this.
It will be very interesting to see if these projections become reality and the importance mobile phones and other devices have on consumers and organisations in the coming years.


After last weekend & this past week of back-to-back courses and events, I'm only too glad that all normalcy has resumed this weekend.
Which probably explain the crazy smiles you see on Liam's face. I did nothing of the "look here and SMILE! SMILE Liam. Here" sort and well, you can tell how happy he was just having his folks around the entire weekend!