#python #bot #bot_framework #botkit #bots #chatbot #chatbots #chatbots_framework #conversation_driven_development #conversational_agents #conversational_ai #conversational_bots #machine_learning #machine_learning_library #mitie #natural_language_processing #nlp #nlu #rasa #spacy #wit

Rasa is an open-source framework that helps build advanced chatbots. It allows developers to create contextual assistants that can have layered conversations, making interactions more natural. Rasa supports integration with various platforms like Facebook Messenger, Slack, and Google Home Actions. This flexibility and customization capability make it a popular choice for businesses to automate customer support and enhance user experience. By using Rasa, users can create intelligent chatbots that understand and respond to user inputs effectively, improving communication and engagement.

https://github.com/RasaHQ/rasa

#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 #asr #deeplearning #generative_ai #large_language_models #machine_translation #multimodal #neural_networks #speaker_diariazation #speaker_recognition #speech_synthesis #speech_translation #tts

NVIDIA NeMo is a powerful, easy-to-use platform for building, customizing, and deploying generative AI models like large language models (LLMs), vision language models, and speech AI. It lets you quickly train and fine-tune models using pre-built code and checkpoints, supports the latest model architectures, and works on cloud, data center, or edge environments. NeMo 2.0 is even more flexible and scalable, with Python-based configuration and modular design, making it simple to experiment and scale up. The main benefit is that you can create advanced AI applications faster, with less effort, and at lower cost, while getting high performance and easy deployment options[1][2][3].

https://github.com/NVIDIA/NeMo

#typescript #api_client #hub #huggingface #inference #machine_learning

Hugging Face offers JavaScript libraries that let you easily use over 100,000 AI models for tasks like text generation, image creation, translation, and more, directly in your code or browser. You can create and manage model repositories, upload files, and run AI tasks such as chat completions or text-to-image generation with simple commands. These libraries work on modern environments without extra dependencies and support multiple providers, giving you flexible access to powerful AI tools. This helps you quickly add advanced AI features to your projects without deep AI expertise or complex setup.

https://github.com/huggingface/huggingface.js

#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

#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

#typescript #agents #ai #embedders #genkit #llm #machine_learning #multimodal #rag #vector_database

Genkit is an open-source framework by Google Firebase that helps you easily build AI-powered apps using a single interface to connect many AI models like Google Gemini, OpenAI, and Anthropic. It supports JavaScript/TypeScript (stable), Go (beta), and Python (alpha), letting you create chatbots, automations, and recommendations quickly with simple code. Genkit works well with web and mobile platforms, offers tools for testing and debugging AI features locally, and lets you deploy and monitor your AI apps on Firebase or other cloud services. This saves you time and effort in developing and managing AI applications efficiently.

https://github.com/firebase/genkit

#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

#python #aws #aws_cli #aws_sdk #cloud #cloud_management #cloudformation #cloudwatch #dynamodb #ec2 #ecs #elasticsearch #iam #kinesis #lambda #machine_learning #rds #redshift #route53 #s3 #serverless

AWS Lambda lets you run code without managing servers, automatically scaling to handle any number of requests and charging you only for the compute time you use. It supports many programming languages and integrates well with other AWS services, making it ideal for tasks like real-time data processing, image handling, chatbots, and automating backups. This serverless approach saves you time and money by removing infrastructure management and adapting instantly to demand spikes, so your applications stay responsive and cost-efficient even as usage changes. Lambda is great for building scalable, event-driven applications quickly and easily.

https://github.com/donnemartin/awesome-aws

#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