Neural Networks
Perceptrons
Perceptrons are the simplest type of neural network, consisting of a single layer of neurons used for binary classification tasks. This category covers the basic structure of perceptrons, including input nodes, weights, a bias term, and an activation function. Perceptrons form the building blocks for more complex neural networks and are used to understand the fundamentals of how neurons process and transmit information. They are foundational for learning more advanced models.
Multilayer Perceptrons (MLPs)
Multilayer Perceptrons (MLPs) are neural networks with multiple layers, including input, hidden, and output layers. This category explains how MLPs can model complex relationships in data through multiple layers of neurons with non-linear activation functions. MLPs are versatile and can be used for both classification and regression tasks. They are applied in various fields, including finance, healthcare, and engineering, for tasks like predicting stock prices, diagnosing diseases, and modeling physical systems.
Convolutional Neural Networks (CNNs)
Convolutional Neural Networks (CNNs) are designed for processing grid-like data, such as images. This category describes the key components of CNNs, including convolutional layers that automatically learn spatial hierarchies of features, pooling layers that reduce dimensionality, and fully connected layers that perform classification. CNNs are highly effective for image-related tasks like image recognition, object detection, and facial recognition, due to their ability to capture spatial patterns.
Recurrent Neural Networks (RNNs)
Recurrent Neural Networks (RNNs) are tailored for sequence data, allowing them to capture temporal dependencies in data. This category covers the basic structure of RNNs, as well as advanced variants like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) that address issues of long-term dependencies and vanishing gradients. RNNs are widely used in applications such as language modeling, machine translation, speech recognition, and time series forecasting.
Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs) consist of two neural networks, a generator and a discriminator, that compete against each other. This category explains how the generator creates fake data samples while the discriminator evaluates them, with both networks improving through this adversarial process. GANs are used for generating realistic images, videos, and audio, as well as for data augmentation and style transfer in creative fields.
Autoencoders
Autoencoders are neural networks used for unsupervised learning tasks like dimensionality reduction and data compression. This category covers the architecture of autoencoders, including the encoder that compresses the data into a latent space and the decoder that reconstructs it. Variational Autoencoders (VAEs) extend this concept by generating new data samples. Autoencoders are applied in tasks such as anomaly detection, image denoising, and feature extraction.