Neural Networks

Introduction They consist of interconnected layers of nodes, or “neurons,” that process and transmit information. These networks can learn from vast amounts of data, making them powerful tools for tasks such as image and speech recognition, natural language processing, and predictive analytics. Where did this idea come from? Neural networks, tools for machine learning, are…

Unlike traditional sequential architectures like RNNs, transformers process all inputs simultaneously, allowing for greater parallelization and efficiency. To be continued …

Consisting of two networks (generator and discriminator) that contest with each other, GANs are used for generating new data instances that resemble the training data. To be continued …

Designed for sequential data, these networks have connections that loop back on themselves, allowing them to maintain memory of previous inputs. This makes them ideal for tasks like language modeling and time series prediction. To be continued …

Primarily used for image processing, CNNs apply convolutional layers that automatically detect spatial hierarchies and features through local receptive fields and shared weights. To be continued …

The simplest type of neural network where connections between the nodes do not form cycles. Information moves in one direction—from the input layer through the hidden layers to the output layer. To be continued…