text: Last Updated: Mar 27, 2024
  
  Leveraging ChatGPT - GenAI as a Microsoft Data Expert
  Speaker
  Prerita Agarwal
  Data Specialist @
  23 Jul, 2024 @ 01:30 PM
  
  ## Introduction
  
  Letâ€™s understand the situation with the help of an example. In this example data set, we have four columns X1 X2 X3, X4 is the input, and Y is the output column.
  
  In this data set, we can see that the X1 column is a Sparse column. Most data points are zero, and very few are non-zero. All the remaining columns are dense. That is, most of the values are non-zero.
  
  If you want to apply multiple regression in this data set, we will identify a hyperplane that gives the minimum loss.
  
  The hyperplane is This is the plane that we will identify that gives the minimum loss. We are randomly selecting some weights, and we are updating using this equation
  
  For understanding purposes, we are updating the weights for w1. We have to update all the Weights that are w2 w3 w4 and so on. Here Î· Is called the learning rate, and L is the loss function. For a Greater understanding of the equation, I am dropping here a link that will redirect you to implementing the Gradient Descent algorithm. We used loss function to mean a squared loss in case of multiple regression. The partial differentiation of loss function concerning w1 is
  
  Now take a look at the data set. Most of the values in X1 are zeros. If X1 is zero, the derivative value will be zero. So, in this case, our weights are not getting updated. That is the main problem in the case of sparse data. When we discussed the Gradient Descent algorithm, We randomly selected some weights, and it keeps on updating and keeps on going to the minimum point.
  
  Source: medium.com
  
  So what we can observe in this 2D graph is that we are initially taking long steps, and when it is going to the local minimum, it is taking smaller steps compared to the previous. So if we take smaller steps close to the minimum point, there is a high chance of getting the actual point. The learning rate determines the measures taken. If the learning rate is significant, the steps taken are giant and vice-versa.
  
  Now coming to the Neural networks, we are updating lots of weights. So if some of the column data are sparse and some are dense, then some of the dimensions update quickly, and some are not. To avoid this problem, we are using the AdaGrad Optimizer. The idea here is that we will take the different learning rates for additional weights. The second point is that the learning rate decreases based on previous updates.
  
  t' is the different learning rates for different dimensions. I am showing the equation for the update In only one size. Rest other follows the same equation.
  
  K Value depends on the previous gradients. Why are we considering previous Gradients? The Update is based on Gradients. zero Gradient means, there is no need to update, and if the Gradient is not zero, we have to reduce the learning rate.
  
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  The drawback of the AdaGrad Optimizer is using the square value of gradients, i.e., . If the number of updates increases, the K value will keep increasing. When the k value becomes vast, the denominator of the expression will be very, very small. Hence our weights will converge very slowly. To avoid this, we don't let K become very large. That is the idea of the AdaDelta optimizer.
  
  The AdaDelta optimizer uses the concept of Exponential Weighted Average. The equation is
  
  In exponentially weighted average, Based on previously selected gradients, Only a few points will be considered; not all the Gradients are considered. So in this way, the AdaDelta optimizer removes the drawback of the AdaGrad Optimizer.
  
  1. What is the significance of the learning rate used in the Gradient descent algorithm?
  In the Gradient descent algorithm, the learning rate is used to determine the size of steps taken to reach the local minimum point of the loss function.
  
  2. What do we understand by Gradient?
  Gradient is the measure of the slope of a straight line. Gradients can be uphill in direction or Downhill in order. The Uphill gradients take positive values, and the Downhill gradients take the negative value.
  
  1. Manual tuning of learning rate is not required
  2. Convergence is faster
  
  4. What are the types of Gradient Descent?
  
  ## Key Takeaways
  
  Apart from AdaDelta and Adagrad, various optimizers are available in neural networks. If you are interested in learning them in great detail, you must visit here.
  
  Topics covered
  1.
  Introduction
  2.
explanation: The document is a technical explanation of the Gradient Descent algorithm and its variants, such as AdaGrad and AdaDelta, in the context of machine learning and neural networks. It includes a brief introduction to the problem of sparse data in regression and how different optimizers address this issue. The document also contains a few questions and their answers related to the learning rate and gradients.
distilabel_metadata.content: Evaluate the user given text extract and determine if it contains a math question and optionally and answer to this question.
  
  Output rules:
  
  1. Briefly describe the document and mention if it contains a question or a question and answer.
  2. Finalize the output with: Label: no-question,question,question-and-answer,multiple-question,multiple-question-and-answer
distilabel_metadata.role: system
distilabel_metadata.content: Last Updated: Mar 27, 2024
  
  Leveraging ChatGPT - GenAI as a Microsoft Data Expert
  Speaker
  Prerita Agarwal
  Data Specialist @
  23 Jul, 2024 @ 01:30 PM
  
  ## Introduction
  
  Letâ€™s understand the situation with the help of an example. In this example data set, we have four columns X1 X2 X3, X4 is the input, and Y is the output column.
  
  In this data set, we can see that the X1 column is a Sparse column. Most data points are zero, and very few are non-zero. All the remaining columns are dense. That is, most of the values are non-zero.
  
  If you want to apply multiple regression in this data set, we will identify a hyperplane that gives the minimum loss.
  
  The hyperplane is This is the plane that we will identify that gives the minimum loss. We are randomly selecting some weights, and we are updating using this equation
  
  For understanding purposes, we are updating the weights for w1. We have to update all the Weights that are w2 w3 w4 and so on. Here Î· Is called the learning rate, and L is the loss function. For a Greater understanding of the equation, I am dropping here a link that will redirect you to implementing the Gradient Descent algorithm. We used loss function to mean a squared loss in case of multiple regression. The partial differentiation of loss function concerning w1 is
  
  Now take a look at the data set. Most of the values in X1 are zeros. If X1 is zero, the derivative value will be zero. So, in this case, our weights are not getting updated. That is the main problem in the case of sparse data. When we discussed the Gradient Descent algorithm, We randomly selected some weights, and it keeps on updating and keeps on going to the minimum point.
  
  Source: medium.com
  
  So what we can observe in this 2D graph is that we are initially taking long steps, and when it is going to the local minimum, it is taking smaller steps compared to the previous. So if we take smaller steps close to the minimum point, there is a high chance of getting the actual point. The learning rate determines the measures taken. If the learning rate is significant, the steps taken are giant and vice-versa.
  
  Now coming to the Neural networks, we are updating lots of weights. So if some of the column data are sparse and some are dense, then some of the dimensions update quickly, and some are not. To avoid this problem, we are using the AdaGrad Optimizer. The idea here is that we will take the different learning rates for additional weights. The second point is that the learning rate decreases based on previous updates.
  
  t' is the different learning rates for different dimensions. I am showing the equation for the update In only one size. Rest other follows the same equation.
  
  K Value depends on the previous gradients. Why are we considering previous Gradients? The Update is based on Gradients. zero Gradient means, there is no need to update, and if the Gradient is not zero, we have to reduce the learning rate.
  
  Get the tech career you deserve, faster!
  Connect with our expert counsellors to understand how to hack your way to success
  User rating 4.7/5
  1:1 doubt support
  95% placement record
  Akash Pal
  Senior Software Engineer
  326% Hike After Job Bootcamp
  Himanshu Gusain
  Programmer Analyst
  32 LPA After Job Bootcamp
  After Job
  Bootcamp
  
  The drawback of the AdaGrad Optimizer is using the square value of gradients, i.e., . If the number of updates increases, the K value will keep increasing. When the k value becomes vast, the denominator of the expression will be very, very small. Hence our weights will converge very slowly. To avoid this, we don't let K become very large. That is the idea of the AdaDelta optimizer.
  
  The AdaDelta optimizer uses the concept of Exponential Weighted Average. The equation is
  
  In exponentially weighted average, Based on previously selected gradients, Only a few points will be considered; not all the Gradients are considered. So in this way, the AdaDelta optimizer removes the drawback of the AdaGrad Optimizer.
  
  1. What is the significance of the learning rate used in the Gradient descent algorithm?
  In the Gradient descent algorithm, the learning rate is used to determine the size of steps taken to reach the local minimum point of the loss function.
  
  2. What do we understand by Gradient?
  Gradient is the measure of the slope of a straight line. Gradients can be uphill in direction or Downhill in order. The Uphill gradients take positive values, and the Downhill gradients take the negative value.
  
  1. Manual tuning of learning rate is not required
  2. Convergence is faster
  
  4. What are the types of Gradient Descent?
  
  ## Key Takeaways
  
  Apart from AdaDelta and Adagrad, various optimizers are available in neural networks. If you are interested in learning them in great detail, you must visit here.
  
  Topics covered
  1.
  Introduction
  2.
distilabel_metadata.role: user
distilabel_metadata.raw_output_q_a_detector_0: The document is a technical explanation of the Gradient Descent algorithm and its variants, such as AdaGrad and AdaDelta, in the context of machine learning and neural networks. It includes a brief introduction to the problem of sparse data in regression and how different optimizers address this issue. The document also contains a few questions and their answers related to the learning rate and gradients.
  
  Label: multiple-question-and-answer