Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.

In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time. You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate. You will also learn about adaptive learning rate techniques like AdaGrad, RMSprop, and Adam which can also help speed up your training.

Complete guide on deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets

In this course you are going to look at advanced NLP. It will show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know. nWord2vec is interesting because it magically maps words to a vector space where you can find analogies, like:king — man = queen — woman, France — Paris = England — London, December — Novemeber = July — June. You are also going to look at the GLoVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems. Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train. You will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity.

Lastly, you’ll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words.

Complete guide to artificial intelligence and machine learning, prep for deep reinforcement learning

When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing — playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more.And yet reinforcement learning opens up a whole new world. As you’ll learn in this course, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other. It’s led to new and amazing insights both in behavioral psychology and neuroscience. In this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It’s the closest thing we have so far to a true general artificial intelligence.

Go hands-on with the latest neural network, artificial intelligence, and data science techniques employers are seeking.

If you’ve got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry — and prepare you for a move into this hot career path.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won’t find academic, deeply mathematical coverage of these algorithms in this course — the focus is on practical understanding and application of them. At the end, you’ll be given a final project to apply what you’ve learned.

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. It’ll cover the machine learning and data mining techniques real employers are looking for, including: Deep Learning / Neural Networks (MLP’s, CNN’s, RNN’s), Regression analysis, K-Means Clustering, Principal Component Analysis, Train/Test and cross validation, Bayesian Methods, Decision Trees and Random Forests, Multivariate Regression, Multi-Level Models, Support Vector Machines, Reinforcement Learning, Collaborative Filtering, K-Nearest Neighbor, Bias/Variance Trade off, Ensemble Learning, Term Frequency / Inverse Document Frequency, Experimental Design and A/B Tests and much more.

In this course, we will start from the very scratch. This is a very applied course, so we will immediately start coding even without installation! You will see a brief bit of absolutely essential theory and then we will get into the environment setup and explain almost all concepts through code. You will be using Keras — one of the easiest and most powerful machine learning tools out there.

You will start with a basic model of how machines learn and then move on to higher models such as:

Convolutional Neural Networks

Residual Connections

Inception Module

All with only a few lines of code. All the examples used in the course comes with starter code which will get you started and remove the grunt effort. The course also includes finished codes for the examples run in the videos so that you can see the end product should you ever get stuck.

This course is divided into days, but of course you can learn at your own pace. In Day 2 we teach you all the fundamentals of the Python programming language. If you already have experience coding in this popular language, brushing up on the fundamentals and fixing bad coding habits is a great exercise. If you are a beginner this section ensures you do not get lost with the rest of the crowd.

At Day 3 we dive into machine learning and neural networks.

You also get an introduction to convolutions. These are hot topics that are in high demand in the market. If you can use this new technology to your advantage you are pretty much guaranteed a job! Everyone is desperate for employees with these skills.

In Day 4 we go headfirst into Keras and understanding the API and Syntax.

You also get to know TensorFlow, the open source machine learning framework for everyone.

At Day 5 we explore the CIFAR-10 image dataset. Then we are ready to build our very own image classifier model from scratch. You will learn how to classify images by training a model.