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

The video tutorial explains how to build a neural network with two computational layers and one output unit. It covers the initialization of weights, the implementation of the forward step, and the understanding of input and output dimensions. The tutorial also introduces the concept of activation functions, which will be implemented in the next video.

How many computational layers, including the output layer, are in the example neural network?

• 2

• 3

• 4

• 5

What is the size of the weight matrix for the first computational layer if the input dimension is 10?

• 10 by 2

• 10 by 3

• 3 by 10

• 2 by 10

In the second computational layer, how many neurons are there?

• 4

• 3

• 2

• 1

What does the forward step function primarily do?

• Sets the input dimensions

• Calculates the output of each layer

• Initializes weights

• Defines the number of neurons

What is the output of the forward step function in the given example?

• A tensor with five numbers

• A single number

• A matrix

• A vector with two numbers

If the last layer has 5 neurons, what will the output be?

• A matrix

• A single number

• A tensor with ten numbers

• A vector with five numbers

What topic is introduced at the end of the video?

• Loss functions

• Backward propagation

• Activation functions

• Weight initialization