Learning Deep Learning with PyTorch: A Hands-on Approach

Deep learning can seem daunting for beginners, especially those with limited coding experience. However, one effective approach is to start with simple, concrete examples and gradually build understanding through experimentation. Let’s explore this methodology using PyTorch as our framework.

The journey begins with basic gradient descent implementation. Consider a simple example with just two parameters and a straightforward loss function:

import torch
w = torch.tensor([0.5,0.5], requires_grad=True)
loss = w[0]**2 + 2*w[1]**2
optimizer = torch.optim.SGD([w], lr=0.01)
loss.backward()
optimizer.step()

This basic implementation allows us to observe how parameters update during training. By examining the gradients and parameter values at each step, we can verify our theoretical understanding matches the actual computations.

Building on this foundation, we can explore more sophisticated optimizers. PyTorch offers several options, each with unique characteristics:

The Adagrad optimizer adapts learning rates for each parameter based on historical gradient information. Parameters with larger gradients get smaller learning rates and vice versa. This approach helps balance the learning process across different parameters.

RMSprop improves upon Adagrad by introducing an exponentially decaying average of squared gradients. This prevents the learning rates from becoming too small over time, a common issue with Adagrad.

The Adam optimizer combines the benefits of momentum-based methods with adaptive learning rates. It maintains both a moving average of gradients and their squares, effectively incorporating the advantages of both RMSprop and momentum methods.

Learning rate scheduling represents another crucial aspect of optimization. PyTorch provides various scheduling strategies:

• Step-based scheduling
• Exponential decay
• Plateau-based adaptive scheduling
• Custom scheduling functions

Through hands-on experimentation with these different optimizers and scheduling strategies, we can develop an intuitive understanding of their behaviors and trade-offs. This practical approach helps bridge the gap between theoretical knowledge and practical implementation skills.

Remember that while these examples use simplified scenarios, the underlying principles apply to larger, more complex models. Starting with basic examples allows us to focus on understanding core concepts without getting overwhelmed by implementation details.

The iterative process of modifying code, predicting outcomes, and verifying results helps develop both coding proficiency and deep learning intuition. As you gain confidence with these fundamentals, you can progressively tackle more complex architectures and applications using PyTorch.