Awesome GitHub RepositoriesTraining Notebooks

Unsloth is a high-performance training and inference platform designed to optimize the lifecycle of large language and multimodal models. It provides a comprehensive engine for fine-tuning, executing, and managing models locally, with a focus on reducing memory consumption and increasing compute speed on consumer-grade hardware. The platform distinguishes itself through hand-optimized kernels and automated computational graph techniques that maximize hardware throughput. It supports advanced training methodologies, including reinforcement learning for reasoning and efficient adapter-based fin

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

Dieses Projekt ist ein umfassendes Bildungsprogramm und Deep-Learning-Framework, das darauf ausgelegt ist, praktisches Deep Learning mit PyTorch anhand von Notebooks und Codebeispielen zu vermitteln. Es dient als High-Level-Bibliothek zum Erstellen, Trainieren und Bereitstellen neuronaler Netze und fungiert als Modell-Trainings-Orchestrator, der PyTorch-Modelle, Optimierer und Verlustfunktionen koordiniert. Das Projekt bietet spezialisierte Toolkits für Computer Vision, Natural Language Processing und die Vorverarbeitung tabellarischer Daten. Es zeichnet sich durch fortschrittliche Trainingskontrollen aus, wie z. B. diskriminative Lernraten, ein Zwei-Wege-Callback-System zur Anpassung der Trainingslogik und eine High-Level-Learner-Abstraktion, die die Geräteplatzierung und Trainingsschleifen automatisiert. Das Framework deckt ein breites Fähigkeitsspektrum ab, einschließlich der automatisierten Konstruktion von Datenpipelines, der Analyse von Modellarchitekturen und der Leistungsbewertung bei Klassifizierungs-, Regressions- und Segmentierungsaufgaben. Es enthält zudem Dienstprogramme für verteiltes Training über mehrere GPUs, Mixed-Precision-Training zur Speicheroptimierung und spezialisierte Unterstützung für medizinische Bilddaten. Das Projekt wird als eine Reihe von Jupyter Notebooks bereitgestellt.

This project is a web development curriculum providing a structured set of instructional materials and guided exercises for learning programming languages and frameworks. It functions as a technical training resource that hosts programming learning paths, including the creation of to-do applications, message boards, and browser games. The project focuses on making programming education accessible through localized educational content served in multiple languages. It also provides dedicated workshop teacher resources, including training decks, cheat sheets, and presentation templates to assist

This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen