#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

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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

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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

#jupyter_notebook #chirp #gemini #google_cloud #imagen #lyria #nano_banana #veo #vertex_ai

GenMedia Creative Studio is a web app that lets you use Google Cloud’s generative AI tools to create images, videos, music, and speech. It includes features like Imagen for images, Veo for videos, Lyria for music, and Chirp for speech, plus creative workflows for tasks like virtual try-ons and moodboards. You can experiment with these tools to quickly make and test creative media, helping you bring new ideas to life faster and more easily.

https://github.com/GoogleCloudPlatform/vertex-ai-creative-studio

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DINOv3 offers powerful self-supervised vision models from Meta AI, like ViT up to 7B parameters and ConvNeXt, pretrained on 1.7B web or satellite images. Load them easily via PyTorch Hub, Hugging Face Transformers (v4.56+), or timm (v1.0.20+), with code examples for features, depth, detection, and segmentation. You benefit by using these top-performing, dense features without fine-tuning or labels—saving time and compute for tasks like classification, object detection, and zero-shot analysis on your images.

https://github.com/facebookresearch/dinov3

#jupyter_notebook #agent #agentic_ai #agents #authentication #bedrock #core #gateway #identity_management #memory_management #production_code #runtime

Amazon Bedrock AgentCore lets you build, deploy, and run AI agents securely at scale with any framework like CrewAI or LangGraph and any model, without managing complex infrastructure. It offers serverless runtime for long tasks up to 8 hours, gateway to connect tools like Slack or APIs easily, memory for personalized experiences, identity management, built-in code interpreter and browser tools, plus observability. This saves time by skipping heavy setup, speeds prototypes to production, cuts costs with pay-per-use, and boosts security—helping you create powerful agents faster for real business needs.

https://github.com/awslabs/amazon-bedrock-agentcore-samples

#jupyter_notebook #chinese_llm #chinese_nlp #finetune #generative_ai #instruct_gpt #instruction_set #llama #llm #lora #open_models #open_source #open_source_models #qlora

AirLLM is a tool that lets you run very large AI models on computers with limited memory by using a smart layer-by-layer loading technique instead of traditional compression methods. You can run a 70-billion-parameter model on just 4GB of GPU memory, or even a 405-billion-parameter model on 8GB, without losing model quality. The benefit is that you can use powerful AI models on affordable hardware without expensive upgrades, and the tool also offers optional compression features that can speed up performance by up to 3 times while maintaining accuracy.

https://github.com/lyogavin/airllm

#jupyter_notebook #aiagent #chatgpt #finance #fingpt #large_language_models #multimodal_deep_learning #prompt_engineering #robo_advisor

FinRobot is a free open-source platform using AI agents and large language models for easy financial analysis. It automates stock predictions, equity reports from 10-K filings, risk checks, valuations like P/E ratios, and trading strategies with real-time data from news and markets. Install via Python, add API keys, and run demos for instant insights. This saves you hours on complex research, delivers pro-level reports and forecasts accurately, and helps make smarter investment decisions without expert skills.

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

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Insanely Fast Whisper is a simple CLI tool that transcribes audio files super quickly on your NVIDIA GPU or Mac using OpenAI's Whisper Large v3 model with optimizations like Flash Attention 2. Install via `pipx install insanely-fast-whisper` and run `insanely-fast-whisper --file-name youraudio.mp3 --flash True` to transcribe 150 minutes of audio in under 98 seconds. You benefit by saving hours on tasks like podcasting or meetings, getting accurate text output fast without cloud costs or slow processing.

https://github.com/Vaibhavs10/insanely-fast-whisper

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This free tutorial series teaches hands-on large model skills like fine-tuning, prompting, math reasoning, jailbreaks, agents, and safety alignment, with slides, guides, code notebooks, and new Huawei Ascend courses (PPTs, videos) for full development workflows. You'll quickly master AI models through simple practices, boosting your course projects, research, or career in large language models.

https://github.com/Lordog/dive-into-llms

#jupyter_notebook #agentmemory #agents #ai #ai_developer #artificial_intelligence #generative_ai #kubernetes #kustomize #oracle_database #oracleaidatabase #oraclejet #rag

Oracle AI Developer Hub offers apps, notebooks, guides, workshops, and agent memory tools to build AI apps, agents, and systems using Oracle AI Database and OCI services. Explore ready examples like FitTracker fitness app, RAG agents, and memory-augmented workflows with LangChain or OpenAI. This saves you time by providing complete code, tutorials, and best practices to quickly create production-grade AI solutions without starting from scratch.

https://github.com/oracle-devrel/oracle-ai-developer-hub

#jupyter_notebook #agent #agent_framework #agents #ai_agents #deployment #genai #generative_ai #langgraph #llm #llms #mlops #production #python #tutorials

Agents Towards Production is a free open-source guide for building AI agents that work in real products. It gives runnable tutorials on memory, tools, search, deployment, security, monitoring, testing, and user interfaces. You can use it to learn faster, build with less guesswork, and move from a simple prototype to a more reliable, scalable agent system.

https://github.com/NirDiamant/agents-towards-production

#jupyter_notebook #estimation #perception #robotics #sensorfusion

GTSAM is an open-source C++ library for robot and vision work that helps with mapping and tracking by using factor graphs and Bayes networks. It also has Python and MATLAB support, so you can use it in more than one language. This helps you build more accurate and efficient systems for navigation, SLAM, and IMU data without starting from scratch.

https://github.com/borglab/gtsam

#jupyter_notebook #gemini #large_language_models #llm #openai #training #transformers

This project shows how to build and train a transformer language model from scratch in PyTorch. It uses the Pile dataset, tokenizes text with tiktoken, and stores tokens in HDF5 files for faster training. The code includes attention, MLP, transformer blocks, training, saving, and text generation. The benefit is that you can learn how LLMs work and train your own small or large model on a single GPU, then use it to generate text for your own tasks.

https://github.com/FareedKhan-dev/train-llm-from-scratch

#jupyter_notebook

NVIDIA Cosmos is an open platform with world models, datasets, and tools for building Physical AI for robots, self-driving cars, and smart systems. Cosmos 3 can understand text, images, and video, and can also generate images, video, sound, and action sequences. This helps you create smarter AI that can plan, predict, simulate, and train faster, with ready paths for research and production.

https://github.com/NVIDIA/cosmos

#jupyter_notebook

This repository shares free, organized study materials for all software engineering courses at the university, covering freshman to senior years. The main benefit to you is reduced exam anxiety because you gain easy access to past questions and notes that help you prepare efficiently. It also teaches you to build real problem-solving skills instead of just memorizing for tests, while fostering a community where students support each other fairly without cheating or unnecessary stress.

https://github.com/LecterChu/nwpu-cram

#jupyter_notebook #quant #quantitative_finance #stock_analysis #trading_algorithms

This project is a Python collection of 100+ Chinese stock trading and investing strategies, with code to copy and test them using real market data. It covers timing, factor, value, and portfolio methods, and uses tools like Pandas, NumPy, Qlib, Backtrader, and data from JQData and Tushare. It helps you learn faster by giving ready-made examples, backtests, charts, and full workflow code, so you can study ideas, test strategies, and build your own quant tools more easily.

https://github.com/hugo2046/QuantsPlaybook

#jupyter_notebook #cluster_management #deep_learning #distributed #llama #llama2 #llm #machine_learning #mlops #pytorch

Higgsfield is a tool for training huge AI models on many GPUs with less setup pain. It helps manage computers, split work across nodes, queue runs, and track experiments, so you can train faster, avoid environment problems, and keep your work more reproducible.

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