Reinforcement Learning and Deep RL Python Theory and Projects - DNN Implementation Gradient Step

The video tutorial discusses a neural network architecture with three layers, detailing the number of neurons in each layer. It explains the implementation of the sigmoid activation function using PyTorch and describes the process of updating parameters through gradient descent. The tutorial concludes with a brief overview of the next steps, which include writing a training function for stochastic gradient descent.

What are the three layers of the computational architecture mentioned in the text?

Describe the sigmoid activation function as defined in the text.

What is the next step mentioned after writing the activation function?

Explain the process of updating parameters as described in the text.

What is the purpose of the update parameters function in the context of gradient descent?