#jupyter_notebook #ai #llm #llms #multi_modal #openai #python #rag

Retrieval-Augmented Generation (RAG) is a technique that helps improve the accuracy of large language models by fetching relevant information from databases or documents. This approach ensures that the model's responses are based on up-to-date and accurate data, reducing errors and "hallucinations" where the model might provide false information. For users, RAG offers more reliable and trustworthy responses, allowing them to verify the sources used to generate those responses. This method also saves resources by avoiding the need to retrain models with new data.

https://github.com/FareedKhan-dev/all-rag-techniques

#jupyter_notebook #jax

Flax is a library for creating neural networks with JAX. It offers a flexible way to build and analyze these networks. The new Flax NNX API makes it easier to work with neural networks by using regular Python objects, which helps in creating, debugging, and analyzing models more efficiently. This means users can express their models in a more intuitive way, making it simpler to develop and modify neural networks. Flax also provides many tools and examples to help users get started quickly.

https://github.com/google/flax

#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

#jupyter_notebook #artificial_intelligence #book #large_language_models #llm #llms #oreilly #oreilly_books

You can learn how to use Large Language Models (LLMs) effectively through the book *Hands-On Large Language Models* by Jay Alammar and Maarten Grootendorst. This book uses nearly 300 custom illustrations to explain key concepts and practical tools for working with LLMs, including tokenization, transformers, prompt engineering, fine-tuning, and advanced text generation. It also provides runnable code examples in Google Colab, making it easy to practice and apply what you learn. This resource helps you understand and build your own LLM applications confidently, saving you time and effort in mastering complex AI technology. It’s highly recommended for anyone wanting hands-on experience with LLMs.

https://github.com/HandsOnLLM/Hands-On-Large-Language-Models

#jupyter_notebook

This course guides you through building and deploying your own AI agents using popular tools like OpenAI Agents SDK, CrewAI, LangGraph, AutoGen, and MCP over six weeks. You’ll learn to create agents that can think, act, and work together, with clear setup instructions for Windows, Mac, and Linux, plus support if you get stuck. The benefit is that you gain hands-on experience in the latest AI agent technology, making you ready to develop smart, autonomous systems for real-world tasks, while also connecting with a helpful community and having fun along the way[1][2][3].

https://github.com/ed-donner/agents

#jupyter_notebook #darknet #pytorch #scaled_yolov4 #yolor #yolov3 #yolov4 #yolov7

YOLOv7 is a powerful tool for detecting objects in images and videos. It is fast, accurate, and can work well on devices with limited power, making it useful for real-time applications like self-driving cars and surveillance systems. YOLOv7 uses advanced techniques like Feature Pyramid Networks to detect objects of different sizes and can handle complex scenes with overlapping objects. This makes it beneficial for users who need quick and precise object detection in various environments.

https://github.com/WongKinYiu/yolov7

#jupyter_notebook

SAM 2 is a powerful new AI model that can quickly and accurately separate objects in both images and videos, even if it has never seen them before. It works in real-time, allowing you to select objects with simple prompts like clicks or boxes and refine the results interactively. This makes tasks like video editing, object tracking, and image annotation much easier and faster. SAM 2’s ability to handle complex scenes and track objects smoothly across video frames helps improve creativity and productivity in many fields, from media production to computer vision research. It is open-source and easy to use with Python and PyTorch.

https://github.com/facebookresearch/segment-anything

#jupyter_notebook

Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability.

https://github.com/langchain-ai/rag-from-scratch

#jupyter_notebook #chatgpt #finance #fingpt #fintech #large_language_models #machine_learning #nlp #prompt_engineering #pytorch #reinforcement_learning #robo_advisor #sentiment_analysis #technical_analysis

FinGPT is an open-source AI tool designed specifically for finance, helping you analyze financial news, predict stock prices, and get personalized investment advice quickly and affordably. Unlike costly models like BloombergGPT, FinGPT can be updated frequently with new data at a low cost, making it more accessible and timely. It uses advanced techniques like reinforcement learning from human feedback to tailor advice to your preferences, such as risk tolerance. You can use FinGPT for tasks like sentiment analysis, robo-advising, fraud detection, and portfolio optimization, helping you make smarter financial decisions with up-to-date insights.

https://github.com/AI4Finance-Foundation/FinGPT

#jupyter_notebook #deep_learning #pytorch

You can learn PyTorch effectively in 20 days with a friendly, well-structured guide designed for those who already know some machine learning basics and have used Keras, TensorFlow, or PyTorch before. The book breaks down PyTorch concepts from easy to hard, with clear examples and practical code you can use right away. It includes a daily plan requiring 30 minutes to 2 hours, covering modeling, core concepts, APIs, and even advanced topics like GPU training and recommendation systems. This approach makes mastering PyTorch easier and faster, helping you build strong skills for deep learning projects and real applications.

https://github.com/lyhue1991/eat_pytorch_in_20_days