Awesome GitHub RepositoriesLayer Parameter Optimization

MLAlgorithms es una biblioteca educativa de algoritmos de machine learning que consiste en modelos predictivos principales implementados desde cero en Python. Sirve como referencia para que los desarrolladores estudien la lógica interna y el funcionamiento matemático de estos modelos a través de implementaciones limpias y minimalistas. El código base se centra en el estudio de la implementación de algoritmos y la educación en machine learning, proporcionando una forma de entender la mecánica interna mediante la construcción de componentes sin depender de bibliotecas externas pesadas. El proyecto utiliza encapsulación orientada a objetos y vectorización basada en NumPy para gestionar el estado del modelo y realizar operaciones matemáticas. Su arquitectura enfatiza la transparencia mediante el uso de lógica en Python puro para implementar primitivas de álgebra lineal e inicialización modular de parámetros.

This repository contains programming assignments and lecture notes from Andrew Ng's foundational deep learning course specialization on Coursera. The materials cover core neural network training techniques including optimization algorithms, normalization methods, regularization approaches, parameter initialization strategies, and learning rate scheduling to improve model convergence and generalization. The coursework explores design principles where successive neural network layers learn progressively more abstract feature representations from input data. It provides guidance on selecting ope

Swift for TensorFlow is a custom toolchain that extends the Swift language with first-class automatic differentiation and differentiable types, enabling gradient-based computation directly within the compiler. It integrates the Swift compiler with TensorFlow runtime and XLA backends, allowing tensor operations to be compiled and executed on hardware-accelerated hardware for high-performance machine learning. The project distinguishes itself through compiler-integrated automatic differentiation that computes gradients of user-defined functions and types during compilation, eliminating the need

PlugNPlay-Modules is a collection of reusable PyTorch computer vision modules and deep learning architectural components. It provides a library of standardized building blocks for constructing neural networks, focusing on attention mechanisms, signal processing layers, and feature fusion modules. The project is distinguished by its extensive variety of attention primitives, covering spatial, channel, and temporal weighting, as well as specialized variants like deformable, frequency-enhanced, and linear-complexity attention. It also implements advanced signal processing tools within the neural