🚀 Meet stefan-jansen/machine-learning-for-trading: a gem from today's GitHub trending list.
🔗 https://github.com/stefan-jansen/machine-learning-for-trading
📝 Code for Machine Learning for Algorithmic Trading, 2nd edition.
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The stefan-jansen/machine-learning-for-trading GitHub repository is a treasure trove of resources for anyone looking to apply machine learning to trading. The repo is based on a book that aims to provide a practical and comprehensive guide to using machine learning in algorithmic trading. With over 150 notebooks, the repository offers a wealth of examples and code to help readers implement the concepts and techniques discussed in the book.
The repository covers a wide range of topics, including
To get the most out of the repository, readers are encouraged to review the
The target audience for this repository includes traders, data scientists, and developers interested in applying machine learning to trading. Whether you're a beginner or an experienced practitioner, the repository has something to offer. So why not join the ML4T Community and start exploring the world of machine learning for trading?
In short, this repository is a must-visit for anyone looking to leverage machine learning for trading strategies - learn by doing, and trade with code.
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/stefan-jansen/machine-learning-for-trading
📝 Code for Machine Learning for Algorithmic Trading, 2nd edition.
──────────────────────────────
The stefan-jansen/machine-learning-for-trading GitHub repository is a treasure trove of resources for anyone looking to apply machine learning to trading. The repo is based on a book that aims to provide a practical and comprehensive guide to using machine learning in algorithmic trading. With over 150 notebooks, the repository offers a wealth of examples and code to help readers implement the concepts and techniques discussed in the book.
The repository covers a wide range of topics, including
data sourcing, financial feature engineering, and portfolio management. It also explores the use of supervised and unsupervised machine learning algorithms for trading, as well as deep learning models like CNN and RNN. The notebooks provide numerous examples of how to work with and extract signals from market, fundamental, and alternative text and image data.To get the most out of the repository, readers are encouraged to review the
notebooks while reading the book. The notebooks are usually in an executed state and often contain additional information not included in the book due to space constraints. The repository also includes installation instructions and configuration files for setting up various conda environments and installing the packages used in the notebooks.The target audience for this repository includes traders, data scientists, and developers interested in applying machine learning to trading. Whether you're a beginner or an experienced practitioner, the repository has something to offer. So why not join the ML4T Community and start exploring the world of machine learning for trading?
In short, this repository is a must-visit for anyone looking to leverage machine learning for trading strategies - learn by doing, and trade with code.
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🧠 Channel: https://t.me/GithubRe
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Github Top Repositories
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🚀 Meet jamwithai/production-agentic-rag-course: a gem from today's GitHub trending list.
🔗 https://github.com/jamwithai/production-agentic-rag-course
📝 No description.
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The jamwithai/production-agentic-rag-course is a hands-on project where you'll build a complete research assistant system that automatically fetches academic papers, understands their content, and answers your research questions using advanced RAG techniques.
This course is designed for learners who want to master AI engineering skills, particularly in building production-grade RAG systems. The system, called The arXiv Paper Curator, uses a
Key features include:
- Automated data pipeline fetching and parsing academic papers from arXiv
- Production BM25 keyword search with filtering and relevance scoring
- Intelligent chunking and hybrid search combining keywords with semantic understanding
- Complete RAG pipeline with local LLM, streaming responses, and Gradio interface
- Production monitoring with Langfuse tracing and Redis caching for optimized performance
- Agentic RAG with LangGraph and Telegram Bot for mobile access
Technical highlights include:
The course is structured into 7 weeks, each focusing on a different aspect of building a production RAG system.
In summary, this course is perfect for those who want to build modern AI systems from the ground up and master in-demand AI engineering skills.
Takeaway: Building a production RAG system is not just about AI, it's about creating a robust search foundation first.
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/jamwithai/production-agentic-rag-course
📝 No description.
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The jamwithai/production-agentic-rag-course is a hands-on project where you'll build a complete research assistant system that automatically fetches academic papers, understands their content, and answers your research questions using advanced RAG techniques.
This course is designed for learners who want to master AI engineering skills, particularly in building production-grade RAG systems. The system, called The arXiv Paper Curator, uses a
foundation-first approach, starting with keyword search foundations and then enhancing with vector search for hybrid retrieval. Key features include:
- Automated data pipeline fetching and parsing academic papers from arXiv
- Production BM25 keyword search with filtering and relevance scoring
- Intelligent chunking and hybrid search combining keywords with semantic understanding
- Complete RAG pipeline with local LLM, streaming responses, and Gradio interface
- Production monitoring with Langfuse tracing and Redis caching for optimized performance
- Agentic RAG with LangGraph and Telegram Bot for mobile access
Technical highlights include:
Docker, FastAPI, PostgreSQL, OpenSearch, and Airflow
The course is structured into 7 weeks, each focusing on a different aspect of building a production RAG system.
In summary, this course is perfect for those who want to build modern AI systems from the ground up and master in-demand AI engineering skills.
Takeaway: Building a production RAG system is not just about AI, it's about creating a robust search foundation first.
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🧠 Channel: https://t.me/GithubRe
Github Top Repositories
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🎯 supermemoryai/supermemory landed on trending. Worth a proper look.
🔗 https://github.com/supermemoryai/supermemory
📝 Memory engine and app that is extremely fast, scalable. The Memory API for the AI era.
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Supermemory is a state-of-the-art memory and context engine for AI that automatically learns from conversations, extracts facts, builds user profiles, and handles knowledge updates. It's designed to give AI tools a persistent memory graph across every conversation, making them smarter over time. With
The key features include:
- Memory: extracts facts from conversations and handles temporal changes, contradictions, and automatic forgetting
- User Profiles: auto-maintained user context with stable facts and recent activity
- Hybrid Search: combines RAG and memory in a single query
- Connectors: auto-sync with real-time webhooks from Google Drive, Gmail, Notion, and more
To get started, you can use the Supermemory app, browser extension, or plugins for various AI tools. For developers, it's easy to integrate with a
In short, Supermemory gives your AI the power of human-like memory - it remembers, so you don't have to.
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/supermemoryai/supermemory
📝 Memory engine and app that is extremely fast, scalable. The Memory API for the AI era.
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Supermemory is a state-of-the-art memory and context engine for AI that automatically learns from conversations, extracts facts, builds user profiles, and handles knowledge updates. It's designed to give AI tools a persistent memory graph across every conversation, making them smarter over time. With
Supermemory, you can use it as a company or personal brain, and it's available as a single API for developers to add memory, RAG, user profiles, and connectors to their agents and apps. The key features include:
- Memory: extracts facts from conversations and handles temporal changes, contradictions, and automatic forgetting
- User Profiles: auto-maintained user context with stable facts and recent activity
- Hybrid Search: combines RAG and memory in a single query
- Connectors: auto-sync with real-time webhooks from Google Drive, Gmail, Notion, and more
To get started, you can use the Supermemory app, browser extension, or plugins for various AI tools. For developers, it's easy to integrate with a
single API and drop-in wrappers for major AI frameworks. Supermemory is also state of the art across major AI memory benchmarks, including LongMemEval, LoCoMo, and ConvoMem. In short, Supermemory gives your AI the power of human-like memory - it remembers, so you don't have to.
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🧠 Channel: https://t.me/GithubRe
Github Top Repositories
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📌 Spotted on GitHub Trending: Open-LLM-VTuber/Open-LLM-VTuber — let's break it down.
🔗 https://github.com/Open-LLM-VTuber/Open-LLM-VTuber
📝 Talk to any LLM with hands-free voice interaction, voice interruption, and Live2D taking face running locally across platforms
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Open-LLM-VTuber is a voice-interactive AI companion that supports real-time voice conversations and visual perception. It features a lively Live2D avatar and can run completely offline on your computer. The project offers
The desktop client has a
Key technical highlights include
The project is suitable for users looking for a personalized AI companion and developers interested in contributing to or customizing the project.
Get your own AI companion today - it's like having a
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/Open-LLM-VTuber/Open-LLM-VTuber
📝 Talk to any LLM with hands-free voice interaction, voice interruption, and Live2D taking face running locally across platforms
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Open-LLM-VTuber is a voice-interactive AI companion that supports real-time voice conversations and visual perception. It features a lively Live2D avatar and can run completely offline on your computer. The project offers
cross-platform support for Windows, macOS, and Linux, and has two usage modes: web version and desktop client. The desktop client has a
transparent background desktop pet mode, allowing the AI companion to accompany you anywhere on your screen. It also supports advanced interaction features like visual perception, voice interruption, touch feedback, and Live2D expressions.Key technical highlights include
extensive model support for Large Language Models, Automatic Speech Recognition, and Text-to-Speech, as well as high customizability through simple module configuration, character customization, and flexible Agent implementation.The project is suitable for users looking for a personalized AI companion and developers interested in contributing to or customizing the project.
Get your own AI companion today - it's like having a
virtual friend by your side!──────────────────────────────
🧠 Channel: https://t.me/GithubRe