Deep Learning

Back to Machine Learning

Neural Networks

Neural networks are the foundation of deep learning, consisting of layers of interconnected nodes (neurons) that process input data to learn representations and make predictions. This category covers the basic architecture of neural networks, including input, hidden, and output layers, as well as activation functions that introduce non-linearity. Neural networks are fundamental in various applications such as image and speech recognition, natural language processing, and more.

Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are specialized neural networks designed for processing grid-like data, such as images. This category explains the core components of CNNs, including convolutional layers, pooling layers, and fully connected layers. CNNs are highly effective for tasks like image classification, object detection, and medical image analysis due to their ability to automatically learn spatial hierarchies of features from input images.

Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs) are designed for sequence data, allowing them to capture temporal dependencies. This category includes explanations of basic RNNs, Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs). RNNs are used in applications such as language modeling, machine translation, and time series forecasting, where understanding the order and context of data points is crucial.

Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) are a class of deep learning models used for generating realistic data samples. This category covers the structure of GANs, consisting of a generator that creates data samples and a discriminator that evaluates them. GANs are used in various applications, including image synthesis, video generation, and data augmentation, by learning to produce data indistinguishable from real samples.

Autoencoders

Autoencoders are neural networks used for unsupervised learning, primarily for dimensionality reduction and data compression. This category explains the architecture of autoencoders, which includes an encoder that compresses the input data and a decoder that reconstructs it. Variational Autoencoders (VAEs) are a type of autoencoder used for generating new data samples. Autoencoders are applied in anomaly detection, image denoising, and feature learning.

Deep Reinforcement Learning

Deep reinforcement learning combines deep learning with reinforcement learning to enable agents to learn optimal behaviors from high-dimensional inputs. This category includes approaches like Deep Q-Networks (DQNs), Asynchronous Advantage Actor-Critic (A3C), and Proximal Policy Optimization (PPO). Deep reinforcement learning is used in applications such as robotics, gaming, and autonomous vehicles, where agents learn to make decisions through trial and error interactions with the environment.