Awesome GitHub RepositoriesGradient-Based Learning

This is a Python machine learning library featuring a collection of core algorithms implemented from scratch to demonstrate foundational AI concepts. It provides a comprehensive toolkit for supervised learning, unsupervised learning, and neural network development. The project is distinguished by its custom implementation of a neural network framework, which includes multi-layer perceptrons with backpropagation, gradient descent, and weight regularization. It also includes a specialized anomaly detection toolkit that identifies outliers and rare events using Gaussian probability distributions

Autograd is an automatic differentiation library and numerical gradient engine for Python. Its primary purpose is to compute the gradients of mathematical functions to enable numerical optimization and the training of mathematical models. The library automates the calculation of derivatives to simplify the implementation of optimization algorithms. This supports activities such as machine learning research, gradient-based learning, and the optimization of numerical models.

clmtrackr is a JavaScript computer vision library designed for facial landmark detection and real-time tracking. It implements Constrained Local Models to identify specific coordinate points on a human face within video feeds or static images. The project functions as a real-time face warping engine and expression analysis tool. It can distort facial images via parametric models to create caricatures or identify and label emotional states such as happiness, sadness, anger, and surprise based on feature coordinates. The library covers a broad range of capabilities including automatic and manu

Cleverhans is an adversarial machine learning library and toolkit designed to generate adversarial examples, incorporate them into training loops, and benchmark the resilience of machine learning models. It provides a gradient-based attack framework for constructing both white-box and black-box attacks to identify model misclassifications. The project includes capabilities for model robustness benchmarking, allowing users to evaluate and verify how models resist evasion attacks and malicious input perturbations. It also facilitates adversarial training to increase a model's resistance to pert

Cleverhans هي مكتبة للتعلم الآلي العدائي (adversarial machine learning) في TensorFlow، تعمل كإطار عمل للهجمات، ومعيار للمتانة، ومكتبة للدفاع. توفر مجموعة من الأدوات لتوليد أمثلة عدائية، واختبار أمان الشبكات العصبية، وتنفيذ آليات حماية لزيادة مرونة النماذج ضد المدخلات الضارة. يركز المشروع على إنشاء مدخلات مضطربة مصممة لخداع نماذج التعلم الآلي لتقديم تنبؤات غير صحيحة. وهو يتيح تقييم استقرار ودقة نماذج التعلم العميق عند تعرضها للضجيج العدائي، مع توفير تطبيقات مرجعية لطرق الهجوم المعروفة لتحديد نقاط الضعف الأمنية. تغطي مجموعة الأدوات توليد الأمثلة العدائية، ودفاع نماذج التعلم الآلي، وقياس متانة الشبكات العصبية. وتستخدم واجهة محايدة للنموذج وتطبيقات هجوم قابلة للتفاضل لتنفيذ اضطرابات قائمة على التدرج وحلقات تحسين تكرارية.

Neuralangelo is a neural surface reconstruction framework that transforms two-dimensional image sequences and multi-view photography into high-fidelity 3D meshes. It implements a pipeline for training neural radiance fields to represent complex scenes as digital geometry. The project utilizes a signed distance function for surface representation and multi-resolution hash encoding to capture both coarse and fine geometric details. It employs differentiable volume rendering and gradient-based eikonal regularization to ensure the learned distance functions remain physically plausible. The syste

Machine-Learning-From-Scratch is an educational repository that provides implementations of fundamental machine learning models built using standard Python programming logic. It serves as a resource for understanding the internal mechanics of common statistical and predictive algorithms by constructing them from the ground up rather than relying on high-level machine learning frameworks. The project distinguishes itself by prioritizing transparency in algorithmic design, utilizing mathematical primitives and vectorized array computations to expose the underlying calculus and statistical logic