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  • If it is convex we use Gradient Descent and if it is concave we use we use Gradient Ascent.[1]
  • When we use the convex one we use gradient descent and when we use the concave one we use gradient ascent.[1]
  • Here, we are going to look into one such popular optimization technique called Gradient Descent.[2]
  • In machine learning, gradient descent is used to update parameters in a model.[2]
  • Let us relate gradient descent with a real-life analogy for better understanding.[2]
  • Batch Gradient Descent: In this type of gradient descent, all the training examples are processed for each iteration of gradient descent.[2]
  • Gradient descent is one of the most popular algorithms to perform optimization and by far the most common way to optimize neural networks.[3]
  • At the same time, every state-of-the-art Deep Learning library contains implementations of various algorithms to optimize gradient descent.[3]
  • By the end of this blog post, you’ll have a comprehensive understanding of how gradient descent works at its core.[3]
  • We will intuitively by the means of gradient descent accomplish a task of rod balancing problem on our finger.[3]
  • At each step, the weight vector (w) is altered in the direction that produces the steepest descent along with the error.[4]
  • Summing over multiple examples in standard gradient descent requires more computation per weight update step.[4]
  • Similar to batch gradient descent, stochastic gradient descent performs a series of steps to minimize a cost function.[5]
  • Gradient Descent is an optimization algorithm used for minimizing the cost function in various machine learning algorithms.[6]
  • Batch Gradient Descent: This is a type of gradient descent which processes all the training examples for each iteration of gradient descent.[6]
  • But if the number of training examples is large, then batch gradient descent is computationally very expensive.[6]
  • Hence if the number of training examples is large, then batch gradient descent is not preferred.[6]
  • Since we need to calculate the gradients for the whole dataset to perform one parameter update, batch gradient descent can be very slow.[7]
  • In mini-batch gradient descent, we calculate the gradient for each small mini-batch of training data.[7]
  • Gradient descent is one of those “greatest hits” algorithms that can offer a new perspective for solving problems.[8]
  • At a theoretical level, gradient descent is an algorithm that minimizes functions.[8]
  • To run gradient descent on this error function, we first need to compute its gradient.[8]
  • Below are some snapshots of gradient descent running for 2000 iterations for our example problem.[8]
  • However, it still serves as a decent pedagogical tool to get some of the most important ideas about gradient descent across the board.[9]
  • However, this gives you a very inaccurate picture of what gradient descent really is.[9]
  • As depicted in the above animation, gradient descent doesn't involve moving in z direction at all.[9]
  • A widely used technique in gradient descent is to have a variable learning rate, rather than a fixed one.[9]
  • The gradient descent varies in terms of the number of training patterns used to calculate errors.[10]
  • Each iteration of the gradient descent uses a single sample and requires a prediction for each iteration.[10]
  • If the gradient descent is running well, you will see a decrease in cost in each iteration.[10]
  • Gradient Descent is an optimization algorithm used to find a local minimum of a given function.[11]
  • Gradient Descent finds a local minimum, which can be different from the global minimum.[11]
  • Gradient Descent needs a function and a starting point as input.[11]
  • As we can see, Gradient Descent found a local minimum here, but it is not the global minimum.[11]
  • Gradient Descent is an iterative process that finds the minima of a function.[12]
  • To get an idea of how Gradient Descent works, let us take an example.[12]
  • Now let us see in detail how gradient descent is used to optimise a linear regression problem.[12]
  • For simplicity, we take a constant slope of 0.64, so that we can understand how gradient descent would optimise the intercept.[12]
  • Gradient descent is an optimization technique that can find the minimum of an objective function.[13]
  • Now it's time to run gradient descent to minimize our objective function.[13]
  • To keep things simple, let's do a test run of gradient descent on a two-class problem (digit 0 vs. digit 1).[13]
  • When running gradient descent, we'll keep learning rate and momentum very small as the inputs are not normalized or standardized.[13]
  • This process is called Stochastic Gradient Descent (SGD) (or also sometimes on-line gradient descent).[14]
  • Gradient descent is by far the most popular optimization strategy used in machine learning and deep learning at the moment.[15]
  • Gradient descent is an optimization algorithm that's used when training a machine learning model.[15]
  • Gradient Descent is an optimization algorithm for finding a local minimum of a differentiable function.[15]
  • The equation below describes what gradient descent does: b is the next position of our climber, while a represents his current position.[15]
  • Gradient descent is an optimization technique commonly used in training machine learning algorithms.[16]
  • With gradient descent, you'll simply look around in all possible directions and take a step in the steepest downhill direction.[16]
  • Mini batch gradient descent allows us to split our training data into mini batches which can be processed individually.[16]
  • On the other extreme, a batch size equal to the number of training examples would represent batch gradient descent.[16]
  • This way is called Gradient Descent and it also follow our downhill strategy.[17]
  • Gradient Descent is one of the most used machine learning algorithms in the industry.[18]
  • And with a goal to reduce the cost function, we modify the parameters by using the Gradient descent algorithm over the given data.[18]
  • Gradient descent was originally proposed by CAUCHY in 1847.[18]
  • Gradient descent using Contour Plot.[18]
  • Gradient descent is an optimization algorithm which is commonly-used to train machine learning models and neural networks.[19]
  • Before we dive into gradient descent, it may help to review some concepts from linear regression.[19]
  • While gradient descent is the most common approach for optimization problems, it does come with its own set of challenges.[19]
  • gradient descent, SGD approximates the true gradient of \(E(w,b)\) by considering a single training example at a time.[20]
  • This is where Gradient Descent comes into the picture.[21]
  • We are first going to look at the different variants of gradient descent.[22]
  • We will also take a short look at algorithms and architectures to optimize gradient descent in a parallel and distributed setting.[22]
  • In machine learning, we use gradient descent to update the parameters of our model.[23]
  • In this post you discovered gradient descent for machine learning.[24]
  • Gradient Descent is an optimizing algorithm used in Machine/ Deep Learning algorithms.[25]
  • The first stage in gradient descent is to pick a starting value (a starting point) for \(w_1\).[26]
  • In machine learning, gradients are used in gradient descent.[26]
  • Note: When performing gradient descent, we generalize the above process to tune all the model parameters simultaneously.[26]
  • Backtracking line search is another variant of gradient descent.[27]
  • Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function.[28]
  • The basic intuition behind gradient descent can be illustrated by a hypothetical scenario.[28]
  • Gradient descent can also be used to solve a system of nonlinear equations.[28]
  • Below is an example that shows how to use the gradient descent to solve for three unknown variables, x 1 , x 2 , and x 3 .[28]

소스

  1. 1.0 1.1 Gradient Ascent vs Gradient Descent in Logistic Regression
  2. 2.0 2.1 2.2 2.3 Machine Learning: What is Gradient Descent?
  3. 3.0 3.1 3.2 3.3 Introduction to Optimization and Gradient Descent Algorithm [Part-1.]
  4. 4.0 4.1 The Ascent of Gradient Descent
  5. Stochastic gradient descent
  6. 6.0 6.1 6.2 6.3 Gradient Descent algorithm and its variants
  7. 7.0 7.1 How to understand Gradient Descent, the most popular ML algorithm
  8. 8.0 8.1 8.2 8.3 An Introduction to Gradient Descent and Linear Regression
  9. 9.0 9.1 9.2 9.3 Intro to optimization in deep learning: Gradient Descent
  10. 10.0 10.1 10.2 What Is Gradient Descent in Deep Learning?
  11. 11.0 11.1 11.2 11.3 Gradient Descent in Java
  12. 12.0 12.1 12.2 12.3 An Easy Guide to Gradient Descent in Machine Learning
  13. 13.0 13.1 13.2 13.3 Gradient Descent in Python: Implementation and Theory
  14. CS231n Convolutional Neural Networks for Visual Recognition
  15. 15.0 15.1 15.2 15.3 Gradient Descent: An Introduction to 1 of Machine Learning’s Most Popular Algorithms
  16. 16.0 16.1 16.2 16.3 Gradient descent.
  17. Gradient Descent in deep learning: a mountain perspective
  18. 18.0 18.1 18.2 18.3 How Does the Gradient Descent Algorithm Work in Machine Learning?
  19. 19.0 19.1 19.2 What is Gradient Descent?
  20. 1.5. Stochastic Gradient Descent — scikit-learn 0.23.2 documentation
  21. Keep it simple! How to understand Gradient Descent algorithm
  22. 22.0 22.1 An overview of gradient descent optimization algorithms
  23. Gradient Descent — ML Glossary documentation
  24. Gradient Descent For Machine Learning
  25. Gradient Descent Explained
  26. 26.0 26.1 26.2 Reducing Loss: Gradient Descent
  27. Stochastic gradient descent
  28. 28.0 28.1 28.2 28.3 Gradient descent

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