Machine Learning Paradigms - Four machine learning training paradigms are visually explained [1] - The paradigms include Transfer Learning, Fine-tuning, Multi-task Learning, and Federated Learning [1]

4 machine learning training paradigms, explained visually:- Transfer Learning- Fine-tuning- Multi-task Learning- Federated Learning https://t.co/q6wnDTQtIn ...

RT Avi Chawla (@_avichawla)Check this!! You can now raise PRs by just writing English prompts.I integrated the @codegen coding agent with Slack and asked it to build a video RAG app.5 minutes later, it raised a PR with fully working code.Completely hands-off! https://t.co/bcQFp6yBDJ ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):Check this!! You can now raise PRs by just writing English prompts.I integrated the @codegen coding agent with Slack and asked it to build a video RAG app.5 minutes later, it raised a PR with fully working code.Completely hands-off! https://t.co/bcQFp6yBDJ ...

Technology Stack - Codegen is used as the coding agent, powered by Claude 4 [1] - Google DeepMind Gemini 1.5 serves as the LLM for video RAG [1] - Streamlit is utilized as the UI [1]

Check this!! You can now raise PRs by just writing English prompts.I integrated the @codegen coding agent with Slack and asked it to build a video RAG app.5 minutes later, it raised a PR with fully working code.Completely hands-off! https://t.co/bcQFp6yBDJ ...

AI Engineering Career Development - Identifies 10 GitHub repositories for building a career in AI engineering [1] - Highlights a 100% free roadmap for AI engineering [1] Key Areas in AI/ML - Covers basics of AI/ML [1] - Includes neural networks [1] - Focuses on research paper implementations [1] - Addresses MLOps [1] - Encompasses LLMs/RAG/Agents [1]

General Information - This document is a recommendation to reshare insightful content related to Data Science (DS), Machine Learning (ML), Large Language Models (LLMs), and Retrieval Augmented Generation (RAGs) [1] - The document highlights 10 free GitHub repositories that can help individuals prepare for a career in AI engineering [1] Resources - The author, Avi Chawla (@_avichawla), shares tutorials and insights on DS, ML, LLMs, and RAGs daily [1]

Links:- ML for Beginners: https://t.co/4BjD3ePOET- AI for Beginners: https://t.co/RMGBL5sRfe- NN Zero to Hero: https://t.co/BGKZvCTGeN- Paper implementations: https://t.co/SN0DH2BLQq- Made with ML: https://t.co/2xrM6s50X0- Hands-on LLMs: https://t.co/KTZUVbsAFY- Advanced RAG techniques: https://t.co/3n1fgpc72t- Agents for Beginners: https://t.co/O52uS8quyh- Agents towards production: https://t.co/3n1fgpc72t- AI Engg. Hub: https://t.co/b2WVNQqcBANote: This roadmap moves toward LLMs, NLP, and AI agents after ...

10 GitHub repos that will set you up for a career in AI engineering (100% free): ...