Sigmoid

ReLU

Tanh

Softmax

Sigmoid

Tanh

ReLU

Softmax

Incremental learning

Online learning

Batch learning

Ensemble learning

Linear Regression

Stochastic Gradient Descent (SGD)

Principal Component Analysis (PCA)

K-Nearest Neighbors (KNN)

Early stopping

Increasing the number of neurons in each layer

Decreasing the number of hidden layers

Decreasing the regularization parameter

Multi-layer Perceptron (MLP)

Convolutional Neural Network (CNN)

Recurrent Neural Network (RNN)

Feedforward Neural Network

The need for extensive feature engineering

Difficulty in handling large datasets

Difficulty in handling variable-length inputs

Difficulty in handling high-dimensional inputs

It is less accurate compared to other regression algorithms

It cannot handle non-linearly separable data

It requires careful tuning of hyperparameters

It is less interpretable compared to other regression algorithms

It requires less training data compared to shallow neural networks

It is computationally less expensive compared to shallow neural networks

It is prone to overfitting

It is more interpretable compared to shallow neural networks

To initialize the weights of the neural network

To compute the gradient of the cost function with respect to the model parameters

To regularize the neural network to prevent overfitting

To adjust the learning rate during training