#cplusplus #cpp #cuda #deep_learning #deep_learning_library #gpu #nvidia

CUTLASS is a powerful tool for high-performance matrix operations on NVIDIA GPUs. It helps developers create efficient code by breaking down complex tasks into reusable parts, making it easier to build custom applications. CUTLASS supports various data types and architectures, including the new Blackwell SM100 architecture, which means users can optimize their programs for different hardware. This flexibility and support for advanced features like Tensor Cores improve performance significantly, benefiting users who need fast computations in fields like AI and scientific computing.

https://github.com/NVIDIA/cutlass

#mdx #deep_learning #hacktoberfest #nlp #transformers

The Hugging Face course teaches you how to use Transformers for natural language processing tasks. You'll learn about the Hugging Face ecosystem, including tools like Transformers, Datasets, Tokenizers, and Accelerate, as well as the Hugging Face Hub. This free course helps you understand how to fine-tune models and share your results. It's beneficial because it provides hands-on experience with popular AI libraries and allows you to build and showcase your own projects on the Hugging Face platform.

https://github.com/huggingface/course

#jupyter_notebook #ai #computer_vision #computervision #deep_learning #deep_neural_networks #deeplearning #machine_learning #opencv #opencv_cpp #opencv_library #opencv_python #opencv_tutorial #opencv3

Learning OpenCV and AI can greatly benefit your career by opening up opportunities in fields like autonomous vehicles, healthcare, and robotics. OpenCV University offers comprehensive courses that teach computer vision and deep learning using frameworks like PyTorch. These courses are project-based, providing hands-on experience with real-world applications. By mastering these skills, you can develop innovative solutions and even start your own AI company. The courses are accessible to beginners and offer lifetime access for continuous learning.

https://github.com/spmallick/learnopencv

#python #ai #artificial_intelligence #cython #data_science #deep_learning #entity_linking #machine_learning #named_entity_recognition #natural_language_processing #neural_network #neural_networks #nlp #nlp_library #python #spacy #text_classification #tokenization

spaCy is a powerful tool for understanding and processing human language. It helps computers analyze text by breaking it into parts like words, sentences, and entities (like names or places). This makes it useful for tasks such as identifying who is doing what in a sentence or finding specific information from large texts. Using spaCy can save time and improve accuracy compared to manual analysis. It supports many languages and integrates well with advanced models like BERT, making it ideal for real-world applications.

https://github.com/explosion/spaCy

#python #agent #ai_societies #artificial_intelligence #communicative_ai #cooperative_ai #deep_learning #large_language_models #multi_agent_systems #natural_language_processing

CAMEL-AI is a community-driven project focused on multi-agent systems. It helps researchers study how AI agents interact and behave in large-scale environments. This platform supports tasks like data generation, task automation, and world simulation. By using CAMEL-AI, users can create complex scenarios where multiple agents collaborate to solve problems or generate synthetic data. The benefits include gaining insights into agent behaviors, improving decision-making processes, and enhancing collaboration among AI entities. It's open-source and easy to install via PyPI.

https://github.com/camel-ai/camel

#python #ai #big_model #data_parallelism #deep_learning #distributed_computing #foundation_models #heterogeneous_training #hpc #inference #large_scale #model_parallelism #pipeline_parallelism

Colossal-AI is a powerful tool that helps make large AI models faster, cheaper, and easier to use. It uses special techniques like parallelism to speed up training on big models without needing expensive hardware. This means users can train complex AI models even on regular computers or laptops, saving time and money. Colossal-AI also supports various applications across industries like medicine, video generation, and chatbots, making it very versatile for developers.

https://github.com/hpcaitech/ColossalAI

#jupyter_notebook #computer_vision #deep_learning #inference #machine_learning #openvino

OpenVINO Notebooks are a collection of interactive Jupyter notebooks that help developers learn and experiment with the OpenVINO Toolkit. These notebooks provide an introduction to OpenVINO basics and show how to optimize deep learning inference using the API. They can be run on various platforms, including Windows, Ubuntu, macOS, and cloud services like Azure ML or Google Colab. This makes it easy for users to get started with AI development without needing extensive hardware knowledge, allowing them to focus on building applications efficiently across different devices.

https://github.com/openvinotoolkit/openvino_notebooks

#jupyter_notebook #cnn #colab #colab_notebook #computer_vision #deep_learning #deep_neural_networks #fourier #fourier_convolutions #fourier_transform #gan #generative_adversarial_network #generative_adversarial_networks #high_resolution #image_inpainting #inpainting #inpainting_algorithm #inpainting_methods #pytorch

LaMa is a powerful tool for removing objects from images. It uses special techniques called Fourier Convolutions, which help it understand the whole image at once. This makes it very good at filling in large areas that are missing. LaMa can even work well with high-resolution images, even if it was trained on smaller ones. This means you can use it to fix photos where objects are in the way, making them look natural and complete again.

https://github.com/advimman/lama

#cplusplus #arm #convolution #deep_learning #embedded_devices #llm #machine_learning #ml #mnn #transformer #vulkan #winograd_algorithm

MNN is a lightweight and efficient deep learning framework that helps run AI models on mobile devices and other small devices. It supports many types of AI models and can handle tasks like image recognition and language processing quickly and locally on your device. This means you can use AI features without needing to send data to the cloud, which improves privacy and speed. MNN is used in many apps, including those from Alibaba, and supports various platforms like Android and iOS. It also helps reduce the size of AI models, making them faster and more efficient.

https://github.com/alibaba/MNN

#python #ai #ai_art #art #asset_generator #chatbot #deep_learning #desktop_app #image_generation #mistral #multimodal #privacy #pygame #pyside6 #python #self_hosted #speech_to_text #stable_diffusion #text_to_image #text_to_speech #text_to_speech_app

AI Runner is a tool that lets you use AI on your own computer without needing the internet. It can do many things like **voice chatbots**, **text-to-image** generation, and **image editing**. You can also make AI personalities for more interesting conversations. It runs fast and securely, keeping your data private. To use AI Runner, you need a good computer with a strong GPU, like an NVIDIA RTX 3060 or better. This helps keep your data safe and makes AI tasks faster.

https://github.com/Capsize-Games/airunner

#python #deep_learning #intel #machine_learning #neural_network #pytorch #quantization

Intel Extension for PyTorch boosts the speed of PyTorch on Intel hardware, including both CPUs and GPUs, by using special features like AVX-512, AMX, and XMX for faster calculations[5][2][4]. It supports many popular large language models (LLMs) such as Llama, Qwen, Phi, and DeepSeek, offering optimizations for different data types and easy GPU acceleration. This means you can run advanced AI models much faster and more efficiently on your Intel computer, with simple setup and support for both ready-made and custom models.

https://github.com/intel/intel-extension-for-pytorch

#jupyter_notebook #android #asr #deep_learning #deep_neural_networks #deepspeech #google_speech_to_text #ios #kaldi #offline #privacy #python #raspberry_pi #speaker_identification #speaker_verification #speech_recognition #speech_to_text #speech_to_text_android #stt #voice_recognition #vosk

Vosk is a powerful tool for recognizing speech without needing the internet. It supports over 20 languages and dialects, making it useful for many different users. Vosk is small and efficient, allowing it to work on small devices like smartphones and Raspberry Pi. It can be used for things like chatbots, smart home devices, and creating subtitles for videos. This means users can have private and fast speech recognition anywhere, which is especially helpful when internet access is limited.

https://github.com/alphacep/vosk-api

#rust #ai #ai_engineering #anthropic #artificial_intelligence #deep_learning #genai #generative_ai #gpt #large_language_models #llama #llm #llmops #llms #machine_learning #ml #ml_engineering #mlops #openai #python #rust

TensorZero is a free, open-source tool that helps you build and improve large language model (LLM) applications by using real-world data and feedback. It gives you one simple API to connect with all major LLM providers, collects data from your app’s use, and lets you easily test and improve prompts, models, and strategies. You can see how your LLMs perform, compare different options, and make them smarter, faster, and cheaper over time—all while keeping your data private and under your control. This means you get better results with less effort and cost, and your apps keep improving as you use them[1][2][3].

https://github.com/tensorzero/tensorzero

#jupyter_notebook #ai #artificial_intelligence #chatgpt #deep_learning #from_scratch #gpt #language_model #large_language_models #llm #machine_learning #python #pytorch #transformer

You can learn how to build your own large language model (LLM) like GPT from scratch with clear, step-by-step guidance, including coding, training, and fine-tuning, all explained with examples and diagrams. This approach mirrors how big models like ChatGPT are made but is designed to run on a regular laptop without special hardware. You also get access to code for loading pretrained models and fine-tuning them for tasks like text classification or instruction following. This helps you deeply understand how LLMs work inside and lets you create your own functional AI assistant, gaining practical skills in AI development[1][2][3][4].

https://github.com/rasbt/LLMs-from-scratch

#other #automl #chatgpt #data_analysis #data_science #data_visualization #data_visualizations #deep_learning #gpt #gpt_3 #jax #keras #machine_learning #ml #nlp #python #pytorch #scikit_learn #tensorflow #transformer

This is a comprehensive, regularly updated list of 920 top open-source Python machine learning libraries, organized into 34 categories like frameworks, data visualization, NLP, image processing, and more. Each project is ranked by quality using GitHub and package manager metrics, helping you find the best tools for your needs. Popular libraries like TensorFlow, PyTorch, scikit-learn, and Hugging Face transformers are included, along with specialized ones for time series, reinforcement learning, and model interpretability. This resource saves you time by guiding you to high-quality, actively maintained libraries for building, optimizing, and deploying machine learning models efficiently.

https://github.com/ml-tooling/best-of-ml-python

#python #data_mining #data_science #deep_learning #deep_reinforcement_learning #genetic_algorithm #machine_learning #machine_learning_from_scratch

This project offers Python code for many basic machine learning models and algorithms built from scratch, focusing on clear, understandable implementations rather than speed or optimization. You can learn how these algorithms work inside by running examples like polynomial regression, convolutional neural networks, clustering, and genetic algorithms. This hands-on approach helps you deeply understand machine learning concepts and build your own custom models. Using Python makes it easier because of its simple, readable code and flexibility, letting you quickly test and modify algorithms. This can improve your skills and confidence in machine learning development.

https://github.com/eriklindernoren/ML-From-Scratch