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.

How many neurons are there in the second layer of the neural network described?

• 1 neuron

• 2 neurons

• 4 neurons

• 3 neurons

What is the primary purpose of the sigmoid activation function in a neural network?

• To calculate loss

• To introduce non-linearity

• To normalize input data

• To initialize weights

Which mathematical operation is used in the sigmoid function to transform the input?

• Addition

• Multiplication

• Exponential

• Logarithm

Why is it important to manage gradients during the parameter update process?

• To maintain initial statistics

• To avoid memory overflow

• To prevent data loss

• To ensure convergence

What is the role of the learning rate in the parameter update process?

• To initialize the network

• To scale the gradient updates

• To determine the number of layers

• To set the number of neurons