A neuron is the basic unit of a neural network, responsible for processing information and transmitting it to other neurons.

Backpropagation is the process of adjusting the weights of a neural network to minimize the error between its predictions and the actual labels.

ReLU (Rectified Linear Unit) is a commonly used activation function in neural networks due to its simplicity and computational efficiency.

A convolutional layer is used to extract local features from the input, such as edges and patterns, by applying a convolution operation.

A pooling layer is used to reduce the dimensionality of the input by combining neighboring values into a single value.

Convolutional Neural Networks (CNNs) are commonly used for image classification due to their ability to extract local features and their high accuracy.

A dropout layer is used to prevent overfitting by randomly dropping out some neurons during training.

Stochastic Gradient Descent (SGD) is a commonly used optimization algorithm for training neural networks due to its simplicity and efficiency.

A batch normalization layer is used to normalize the inputs to the network, which can improve the stability of the network during training.

Recurrent neural networks (RNNs) are designed to process sequential data, such as time series data or text, by maintaining a hidden state that captures information from previous inputs.

Long Short-Term Memory (LSTM) networks are commonly used for natural language processing (NLP) due to their ability to learn long-term dependencies in sequential data.

Attention mechanisms allow neural networks to focus on specific parts of the input, which can improve the performance of tasks such as machine translation and image captioning.

Generative Adversarial Networks (GANs) are commonly used for generative tasks, such as generating images or text, by training two networks, a generator and a discriminator, in an adversarial manner.

Transformer neural networks are designed to process sequential data, such as natural language, by using attention mechanisms to capture long-range dependencies.