[Part 7/20] Advanced PyTorch Techniques for Optimizing Neural Networks
Deep Learning with PyTorch — Part 7/20
Table of Contents
1. Exploring Gradient Clipping in PyTorch
2. Leveraging Learning Rate Schedulers
2.1. Implementing Step Decay
2.2. Benefits of Cyclical Learning Rates
3. Advanced Weight Initialization Methods
4. Utilizing Regularization Techniques for Overfitting
5. Batch Normalization and Its Impact on Model Stability
6. Optimizing Neural Networks with Advanced Optimizers
6.1. Understanding Adam and RMSprop
6.2. Exploring Newer Optimizers like LAMB
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1. Exploring Gradient Clipping in PyTorch
Gradient clipping is a technique used to prevent the exploding gradient problem in neural networks, which can lead to unstable training processes. By capping the gradients during backpropagation, this method ensures that they do not exceed a defined…
