Introduction to Deep Learning
__________ plays a vital role in neural networks, and it is used to introduce non-linearity in neural networks.
An activation function
It implies backpropagating from the output layer to the input layer
Backward pass
It specifies the number of training samples we use in one forward pass and one backward pass
Batch size
It is a subset of Machine Learning inspired by the neural networks in the human brain.
Deep Learning
It is a modern name for artificial neural networks with many layers.
Deep Learning (ANN)
It specifies the number of times the neural network sees our whole training data
Epoch
It implies forward propagation from the input layer to the output layer
Forward pass
It implies the number of passes where one pass = one forward pass + one backward pass
Number of iterations
________ is basically the generalization of the sigmoid function. It is usually applied to the final layer of the network and while performing multi-class classification tasks.
The SoftMax function
_________ is one of the most commonly used activation functions. It scales the values between 0 and 1.
The sigmoid function
Although DL perform better than conventional ML models, it is not recommended to use Deep Learning for smaller datasets.
True - DL is not recommended for smaller datasets.
A neuron can be defined as the basic computational unit of the human brain.
True - It is the basic computational unit of the human brain
Any layer between the input layer and the output layer is called?
hidden layer