Comprehensive coverage of artificial neural networks, from foundational perceptrons to modern deep learning architectures, transformers, and generative AI systems. Updated with peer-reviewed research and practical implementations.
An in-depth exploration of Frank Rosenblatt's perceptron model, the mathematical principles behind single-layer networks, and its historical role in launching the first AI boom of the 1950s.
How neural networks learn: a step-by-step mathematical breakdown of chain rule application, weight updates, and optimization landscapes in modern deep learning training pipelines.
Analysis of He et al.'s breakthrough residual learning framework that enabled training of networks with over 1000 layers, solving the vanishing gradient problem in deep architectures.
From attention is all you need to LLMs: how self-attention mechanisms, positional encoding, and multi-head attention replaced recurrent structures and revolutionized sequence modeling.
Learn how GANs pit a generator against a discriminator to create synthetic data. Includes PyTorch implementation, training stability tips, and applications in image synthesis.
How GNNs model relational data across molecules, social networks, and recommendation systems using message passing and graph convolution operations.
Comprehensive reference of non-linear activation functions, their derivatives, use cases, and impact on gradient flow in deep neural network training.
Exploring the theoretical framework showing how over-parameterized networks behave like linear models during gradient descent, bridging statistics and deep learning theory.
Efficient parameter-efficient fine-tuning techniques that allow adapting billion-parameter models with minimal compute, including adapter modules and low-rank decomposition.