Deep Learning - Computer Vision for Beginners Using PyTorch - Building the First Neural Network

The video tutorial explains how to build a deep neural network using the Torch library. It covers the structure of a neural network, including input, hidden, and output layers, and the use of activation functions like Relu and sigmoid. The tutorial also demonstrates how to define a model and loss function in PyTorch, and how to implement the model using PyTorch's torch.nn and torch.optim packages. The process of stacking layers in a sequential model and applying linear operations and activation functions is detailed, along with the use of optimization methods like stochastic gradient descent.

What is the activation function used in the hidden layer of the neural network described?

• Tanh

• Softmax

• Sigmoid

• ReLU

Which activation function is applied in the output layer of the neural network?

• ReLU

• Softmax

• Sigmoid

• Tanh

In a sequential model, what is the primary operation performed between layers?

• Linear operation

• Convolution

• Pooling

• Dropout

What is the method called when layers are stacked one after another in a neural network?

• Hierarchical models

• Cascading models

• Sequential models

• Parallel models

Which package is used in PyTorch to define the model and perform operations?

• torch.data

• torch.utils

• torch.nn

• torch.optim

Which loss function is mentioned in the pseudo-code example for the PyTorch model?

• Mean squared error

• Huber loss

• Hinge loss

• Cross-entropy loss

What is the purpose of the torch.optim package in PyTorch?

• To define the model architecture

• To perform linear operations

• To access optimization algorithms

• To apply activation functions