Master MCP: The Best Free Learning Resources
1️⃣ Everything you need to know about MCP: The first learning resource is a beginner-friendly introduction to MCP by Replit
https://lnkd.in/djVD73Gz
2️⃣ Model Context Protocol (MCP): A Guide With Demo Project: In this blog, you will be guided through building an MCP-powered PR review server that integrates with Claude Desktop
https://lnkd.in/dXDNbAat
3️⃣ Model Context Protocol (MCP) Hugging Face Course: This free course will take you on a journey, from beginner to informed, in understanding, using, and building applications with MCP
https://lnkd.in/dX5Ja_9m
4️⃣ MCP: Build Rich-Context AI Apps with Anthropic: In this hands-on course, you’ll learn the core concepts of MCP and how to implement it in your AI Application
https://lnkd.in/dxRyjRiW
5️⃣ Official MCP Documents: The official MCP docs are a good resource to learn the fundamentals, a tutorial to create your first MCP server, debugging, and inspection instructions
https://lnkd.in/dqkQ6e_b
6️⃣ Awesome MCP Servers: A curated list of awesome Model Context Protocol (MCP) servers
https://lnkd.in/d2AvkBmb
🌟 You can find more information about each learning resource in this article:
https://lnkd.in/dbDHJnNi
https://lnkd.in/djVD73Gz
https://lnkd.in/dXDNbAat
https://lnkd.in/dX5Ja_9m
https://lnkd.in/dxRyjRiW
https://lnkd.in/dqkQ6e_b
https://lnkd.in/d2AvkBmb
https://lnkd.in/dbDHJnNi
#MCP #ModelContextProtocol #AIApplications #ContextAwareAI #MCPLearning #Anthropic #HuggingFace #Replit #AIIntegration #AIFrameworks #OpenSourceAI #LearnMCP #AIEngineering #PromptEngineering #AIProtocols
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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This GitHub repository is a real treasure trove of free programming books.
Here you'll find hundreds of books on topics like #AI, #blockchain, app development, #game development, #Python #webdevelopment, #promptengineering, and many more✋
GitHub: https://github.com/EbookFoundation/free-programming-books
https://t.me/CodeProgrammer⭐
Here you'll find hundreds of books on topics like #AI, #blockchain, app development, #game development, #Python #webdevelopment, #promptengineering, and many more
GitHub: https://github.com/EbookFoundation/free-programming-books
https://t.me/CodeProgrammer
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Forwarded from Python Courses & Resources
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9 key concepts of artificial intelligence, explained in 7 minutes
- Tokenization
- #TextDecoding
- #PromptEngineering
- Multi Step #AI Agents
- #RAGs
- #RLHF
- #VAE
- #DiffusionModels
- #LoRA
👉 @Python53
- Tokenization
- #TextDecoding
- #PromptEngineering
- Multi Step #AI Agents
- #RAGs
- #RLHF
- #VAE
- #DiffusionModels
- #LoRA
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Forwarded from Data Analytics
Transformers & LLMs Cheatsheet.pdf
1.4 MB
The only LLM cheat sheet you'll ever need 🚀
Covers the main concepts, architectures, and practical applications.
### Basics
- Tokens (tokenization, BPE)
- Embeddings (cosine similarity)
- Attention mechanism (Attention formula, Multi-Head Attention)
### Transformer architecture and its variants
- BERT (models with only an encoder)
- GPT (models with only a decoder)
- T5 (models with an encoder and a decoder)
### Large language models (LLMs)
- Prompting (context length, Chain-of-Thought)
- Pre-training (SFT, PEFT/LoRA)
- Preference tuning (Reward Model, Reinforcement Learning)
- Optimizations (Mixture of Experts, Distillation, Quantization)
### Applications
- LLM-as-a-Judge (LaaJ)
- RAG (Retrieval-Augmented Generation)
- Agents (ReAct)
- Reasoning models (Scaling)
✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
#LLM #AI #MachineLearning #DeepLearning #PromptEngineering #Tech
Covers the main concepts, architectures, and practical applications.
### Basics
- Tokens (tokenization, BPE)
- Embeddings (cosine similarity)
- Attention mechanism (Attention formula, Multi-Head Attention)
### Transformer architecture and its variants
- BERT (models with only an encoder)
- GPT (models with only a decoder)
- T5 (models with an encoder and a decoder)
### Large language models (LLMs)
- Prompting (context length, Chain-of-Thought)
- Pre-training (SFT, PEFT/LoRA)
- Preference tuning (Reward Model, Reinforcement Learning)
- Optimizations (Mixture of Experts, Distillation, Quantization)
### Applications
- LLM-as-a-Judge (LaaJ)
- RAG (Retrieval-Augmented Generation)
- Agents (ReAct)
- Reasoning models (Scaling)
✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
#LLM #AI #MachineLearning #DeepLearning #PromptEngineering #Tech
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