#Tutorials

Comprehensive TensorFlow tutorials for deep learning beginners, covering from linear models to advanced concepts. Updated for TensorFlow 2, includes YouTube videos and Google Colab support for easy access, serving both theoretical and practical needs.

Access a wide array of tutorials on leading computer vision models and methodologies. The repository includes education on models from ResNet and YOLO to more sophisticated types like DETR, Grounded DINO, SAM, and GPT-4 Vision. Gain knowledge on model fine-tuning, instance segmentation, and using object-detection transformers. Through thorough tutorials and practical examples, this resource supports both novices and experts in enhancing their skills across various vision tasks, leveraging platforms like Colab and Kaggle. Empower your computer vision initiatives with comprehensive tutorials and experiments available here.

The project provides a well-organized collection of tutorials, articles, and resources relevant to Machine Learning and Deep Learning. It serves as a hub for educational content across various topics, including Artificial Intelligence, Natural Language Processing, and Statistical Learning. Contributors are encouraged to help expand this list by following the guidelines. The repository also offers targeted resources in languages like R and Python, enhancing skills in Data Science, NLP, and Machine Learning. This structured content spans basic introductions to advanced topics like Reinforcement Learning and Bayesian Machine Learning, offering resources suitable for both newcomers and experienced users.

Access practical tutorials on working with Large Language Models (LLMs) through tools such as LangChain, LlamaIndex, and Pinecone. The project features instructional materials and Python scripts supporting YouTube tutorials on LLM development. It caters to learners interested in AI applications, leveraging platforms like OpenAI and HuggingFace, with flexibility in learning paths and code samples.

Delve into a thorough collection of TensorFlow 2.0 tutorials, winner of the PoweredByTF 2.0 Challenge, covering basic to advanced topics including CycleGAN, GPT, and BERT with video guides. This resource serves learners of all levels with practical examples on MNIST, CIFAR10, and ResNet using the latest TensorFlow features.

Discover a wide array of tutorials and training materials for H2O-3 designed to improve comprehension and utilization of its features. Delve into guides covering aspects like H2O Grid Search, Model Selection, Deep Learning, Stacked Ensembles, and AutoML available in both R and Python. Access up-to-date materials to keep pace with H2O's developments. Benefit from resources for self-improvement or event preparation like H2O World. Engage with the H2O community through Stack Overflow and the H2O Stream Google Group for support and shared insights.

Discover a comprehensive selection of AI repositories, tools, websites, papers, and tutorials, refreshed bi-hourly. This resource delivers noteworthy insights and straightforward access to advanced AI technologies, supporting developers, researchers, and enthusiasts to enhance their understanding and practical application. Learn to leverage open-source projects like TensorFlow, PyTorch, and Transformer models, improve skills through project-based tutorials, and benefit from interactive guides. This catalog serves as a rich resource for those aiming to deepen their machine learning knowledge, develop AI solutions, or explore the forefront of AI advancement.

This project provides C++ tutorials for implementing PyTorch, designed for deep learning researchers. Tutorials are divided into basic, intermediate, and advanced sections, covering topics such as linear regression, CNNs, RNNs, and GANs. It supports macOS, Linux, and Windows, with setup instructions using CMake and Conda. Users can access interactive learning on platforms like Google Colab and Docker for practical deep learning insights.