02 - Deep Neural Networks and Training in PyTorch

The lecture begins with an introduction to deep feedforward networks and their architecture, including the role of input, output, and hidden layers. We then discuss the XOR problem, which involves learning a function that outputs a 1 if either of two binary inputs is 1, but outputs a 0 if both inputs are 0 or both inputs are 1. We show how deep feedforward networks can be used to solve the XOR problem.

Next, we dive into the backpropagation algorithm and gradient descent, which are key components of training deep feedforward networks. We cover the basic concepts of backpropagation, including how it computes gradients and how it is used to update the weights in the network. We also discuss gradient descent, which is the optimization algorithm used to find the weights that minimize the error between the network's output and the desired output.

Finally, we apply the backpropagation and gradient descent algorithms to the XOR problem, showing how deep feedforward networks can learn to solve the problem through iterative training.

The tutorial begins with a brief introduction to PyTorch and its main components, including tensors, ml models.

We then try to solve the XOR problem with pytorch and a small self-made neural network. While doing that, we dive into the implementation details of the neural network in PyTorch, including how to define the network architecture using the nn.Module class, how to specify the loss function and optimizer, and how to train the network.