Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Implementation Gradie

The video tutorial covers the architecture of a neural network with three layers, detailing the number of neurons in each layer. It explains the implementation of the sigmoid activation function and demonstrates how to update parameters using gradient descent. The tutorial concludes with a brief introduction to stochastic gradient descent, which will be covered in the next video.

How many neurons are present in the second layer of the neural network discussed?

• 4 neurons

• 3 neurons

• 2 neurons

• 1 neuron

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

• To initialize weights

• To normalize input data

• To calculate loss

• To introduce non-linearity

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

• Multiplication

• Logarithm

• Exponential

• Addition

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

• To initialize the parameters

• To decrease the number of neurons

• To adjust the step size during updates

• To increase the number of layers

Why is it important to use 'torch.no_grad()' during parameter updates?

• To prevent memory leaks

• To avoid updating the wrong parameters

• To increase the learning rate

• To ensure the update does not affect the computation graph