Debugging Neural Networks with PyTorch

In this post, we’ll see what makes a neural network under perform and ways we can debug this by visualizing the gradients and other parameters associated with model training. We’ll also discuss the problem of vanishing and exploding gradients and methods to overcome them.

Finally, we’ll see why proper weight initialization is useful, how to do it correctly, and dive into how regularization methods like dropout and batch normalization affect model performance.

​🐥 Read the article here.

💪 Check out the GitHub repo here.