Neural Networks

• Artificial Intelligence What are the methods of artificial intelligence and machine learning?

• Feedforward Neural Networks (FNNs) The simplest type of neural network where information travels in one direction, from the input layer, through the hidden layers, to the output layer.
• Recurrent Neural Networks (RNNs) Unlike FNNs, RNNs have connections that form directed cycles. This allows them to maintain an internal state that can process sequences of inputs.
• Convolutional Neural Networks (CNNs) Primarily used for image processing, natural language processing, and other grid-like data inputs. They have convolutional layers that filter inputs for useful information.
• Deep Neural Networks (DNNs) Have multiple layers between the input and output layers (more than 3).
• Deep Belief Networks (DBNs) These are generative graphical models which learn to extract a deep hierarchical representation of the training data.
• Radial basis function Neural Networks (RBFNNs) These use radial basis functions as activation functions. They have an input layer, a hidden layer with non-linear radial symmetric activation functions, and a linear output layer.
• Modular Neural Networks (MNNs) These are complex systems that are used to improve the performance of a system by integrating several neural network modules.
• Keras Einstein summation convention is a compact representation of certain tensor operations. It is used in many libraries, including TensorFlow and PyTorch.
• Keras An open-source, high-level, user-friendly python library used for building and training neural networks that runs on top of TensorFlow. Developed to enable fast experimentation and iteration, and it lowers the barrier to entry for working with deep learning. It adds the flexibility of PyTorch to TensorFlow.