Neural networks are a class of algorithms inspired by the human brain’s structure and functionality. They consist of interconnected nodes, also called artificial neurons, organized in layers. Deep learning is a subset of machine learning that focuses on using neural networks with multiple layers, known as deep neural networks, on learning patterns and representations from data.

Deep learning has gained significant attention and popularity due to its remarkable performance in various tasks such as image recognition, natural language processing, and games like Go and Poker. Some of the key concepts and techniques in neural networks and deep learning include:

1. Feedforward Neural Networks: These are the simplest form of neural networks, consisting of an input layer, one or more hidden layers, and an output layer. Each neuron in one layer is connected to all neurons in the next layer, and these connections have associated weights.
2. Activation Functions: Activation functions introduce non-linearity to neural networks. Common activation functions include ReLU (Rectified Linear Unit), sigmoid, and tanh.
3. Backpropagation: This is the primary training algorithm for neural networks. It involves adjusting the weights of connections between neurons based on the calculated error during the forward pass.
4. Gradient Descent: Gradient descent is an optimization technique used to adjust the weights in a neural network by minimizing the error (loss) function.
5. Convolutional Neural Networks (CNNs): CNNs are specialized neural networks designed for processing grid-like data, such as images and videos. They use convolutional layers to learn hierarchical features automatically.
6. Recurrent Neural Networks (RNNs): RNNs are designed to process data sequences, making them suitable for tasks like natural language processing and time series analysis. They have loops that allow information to be passed from one step to the next.
7. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs): These are specialized types of RNNs that address the vanishing gradient problem and improve the ability of RNNs to capture long-range dependencies in sequences.
8. Transfer Learning: Transfer learning involves using pre-trained models on a large dataset as a starting point for training on a smaller dataset. This can significantly speed up training and improve performance, especially in cases where labeled data is limited.