کتابخانه مهندسی کامپیوتر و پایتون

10 GitHub repos to sleep with as an ai engineer covering ML systems, Agents, RAG, MLOps:

1. Machine Learning for Beginners by Microsoft
→ Start here if you’re brand new to ML.
Covers basic ML concepts in Jupyter notebooks beginner-friendly and visual.
🔗 lnkd.in/gGithBVP

2. Learn PyTorch for Deep Learning
→ A great repo to learn PyTorch - could be a bit outdated but the concepts still applies.
🔗 lnkd.in/d4ptSuUY

3. Hands-on Large Language Models
→ This repo supports the Hands-On LLM book.
Learn everything from basic language models to finetuning with real notebooks.
🔗 lnkd.in/gpvPemeG

4. AI Agents for Beginners
→ A fantastic beginner-friendly course to get started with AI agents.
Free 11-lesson hands-on curriculum - no fluff, just code.
🔗 lnkd.in/gVm5gmv7

5. Prompt Engineering Guide
→ One-stop-shop for prompt engineering.
Guides, papers, lectures, and tons of curated examples.
🔗 lnkd.in/gDt3Sknr

6. LLM Course
→ Hands-on course covering the entire LLM lifecycle — design to deployment.
Includes roadmaps + Colab notebooks.
🔗 lnkd.in/gUPJmWNM

7. GenAI Agents
→ Great tutorials + code for building agent-based LLM systems.
Covers everything from simple tool-using agents to advanced workflows.
🔗 lnkd.in/gaiZgzpA

8. RAG Techniques
→ One of the most comprehensive and dynamic collections of Retrieval-Augmented Generation (RAG) tutorials available
🔗 lnkd.in/dcGtjFgY

9. Made With ML
→ Covers full ML product lifecycle: from design to CI/CD and monitoring. If you’re serious about building production-grade ML systems, this is gold.
🔗 lnkd.in/gn2RhT_m

10. Designing Machine Learning Systems
→ Summaries + code + diagrams from the popular O’Reilly book.
A must-read if you want to architect real-world ML pipelines.
🔗 lnkd.in/g26KNXfb

#هوش_مصنوعی #یادگیری_ماشین

🆔 @programmers_street

❤5

www.tgoop.com/programmers_street/8318

3.09K viewsedited  Jul 17 at 14:51

10 GitHub repos to sleep with as an ai engineer covering ML systems, Agents, RAG, MLOps:

1. Machine Learning for Beginners by Microsoft
→ Start here if you’re brand new to ML.
Covers basic ML concepts in Jupyter notebooks beginner-friendly and visual.
🔗 lnkd.in/gGithBVP

2. Learn PyTorch for Deep Learning
→ A great repo to learn PyTorch - could be a bit outdated but the concepts still applies.
🔗 lnkd.in/d4ptSuUY

3. Hands-on Large Language Models
→ This repo supports the Hands-On LLM book.
Learn everything from basic language models to finetuning with real notebooks.
🔗 lnkd.in/gpvPemeG

4. AI Agents for Beginners
→ A fantastic beginner-friendly course to get started with AI agents.
Free 11-lesson hands-on curriculum - no fluff, just code.
🔗 lnkd.in/gVm5gmv7

5. Prompt Engineering Guide
→ One-stop-shop for prompt engineering.
Guides, papers, lectures, and tons of curated examples.
🔗 lnkd.in/gDt3Sknr

6. LLM Course
→ Hands-on course covering the entire LLM lifecycle — design to deployment.
Includes roadmaps + Colab notebooks.
🔗 lnkd.in/gUPJmWNM

7. GenAI Agents
→ Great tutorials + code for building agent-based LLM systems.
Covers everything from simple tool-using agents to advanced workflows.
🔗 lnkd.in/gaiZgzpA

8. RAG Techniques
→ One of the most comprehensive and dynamic collections of Retrieval-Augmented Generation (RAG) tutorials available
🔗 lnkd.in/dcGtjFgY

9. Made With ML
→ Covers full ML product lifecycle: from design to CI/CD and monitoring. If you’re serious about building production-grade ML systems, this is gold.
🔗 lnkd.in/gn2RhT_m

10. Designing Machine Learning Systems
→ Summaries + code + diagrams from the popular O’Reilly book.
A must-read if you want to architect real-world ML pipelines.
🔗 lnkd.in/g26KNXfb

#هوش_مصنوعی #یادگیری_ماشین

🆔 @programmers_street

BY کتابخانه مهندسی کامپیوتر و پایتون