Elements of Neural Nets: Building Blocks of AI
An essential guide to the foundational components of modern neural networks — activations, cost functions, optimizers, and regularization.
Elements of NeuralNets: Building Blocks of AI
Welcome to Elements of NeuralNets, your essential guide to the building blocks of modern AI systems. This section is dedicated to unraveling the core components that form the backbone of artificial intelligence and neural networks.
From understanding the intricacies of algorithms to exploring the functionalities of various neural network architectures, our goal is to provide a comprehensive yet easy-to-digest overview of AI’s foundational elements.
Core topics covered:
- Activations — ReLU, GELU, Swish, and why they matter
- Cost Functions — Cross-entropy, MSE, and task-specific losses
- Optimizers — SGD, Adam, AdamW, and learning rate scheduling
- Regularization — Dropout, weight decay, batch norm, layer norm
Each guide is crafted with clarity in mind, breaking down complex ideas into understandable segments. Expand your knowledge and get a firm grip on what makes neural networks tick.