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AdaGrad

What is AdaGrad?​

AdaGrad is a family of sub-gradient algorithms for stochastic optimization. Gradient descent is the most commonly used optimization method in machine learning and deep learning algorithms. It's essential for training machine learning models. Below, we'll discuss the three primary types of gradient descent used in modern machine learning and deep learning algorithms, as well as the advantages of using AdaGrad.

Batch Gradient Descent​

Batch Gradient Descent is the simplest type of gradient descent. It calculates the error for each example in the training dataset but only updates the model after all training examples have been evaluated.

Stochastic Gradient Descent​

Stochastic Gradient Descent calculates the error and updates the model for each example in the training dataset. This method is popular for optimization due to its conceptual simplicity and efficient implementation. However, it requires manual tuning of the step size parameter.

Mini Batch Gradient Descent​

Mini Batch Gradient Descent sums up the errors over a smaller number of examples based on the batch size and performs an update for each of these batches. This approach balances the benefits of both Batch and Stochastic Gradient Descent.

AdaGrad: Adaptive Gradient Algorithm​

AdaGrad is an optimization algorithm that dynamically incorporates knowledge of the geometry of the data observed in earlier iterations to perform more informative gradient-based learning. It adapts the learning rate component-wise to the parameters by incorporating knowledge of past observations. This results in larger updates (high learning rates) for parameters related to infrequent features and smaller updates (low learning rates) for frequent ones. AdaGrad is well-suited for dealing with sparse data, such as in natural language processing or image recognition.

Advantages of Using AdaGrad​

  • Eliminates the need to manually tune the learning rate.
  • Faster and more reliable convergence compared to simple Stochastic Gradient Descent, especially when the scaling of the weights is unequal.
  • Not very sensitive to the size of the master step.

Understanding the different types of gradient descent and the benefits of using AdaGrad can greatly improve the performance of your machine learning and deep learning algorithms.

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