Selection for those who want to become a certified Claude architect

Useful resources for preparation in one place 👇

• Registration for certification: https://anthropic.skilljar.com/claude-certified-architect-foundations-access-request

• Training (13 free courses):
https://anthropic.skilljar.com

• Cookbook (examples and practices):
https://github.com/anthropics/anthropic-cookbook

• Exam guide:
https://share.google/0eqIbebzRMUt8KTc8

• Practice questions:
https://claudecertifications.com

• MCP documentation:
https://modelcontextprotocol.io

API documentation:
https://docs.anthropic.com

Useful playbook:
https://drive.google.com/file/d/1luC0rnrET4tDYtS7xe5jUxMDZA-4qNf-/view

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Build a Large Language Model from Scratch! 🚀

This repository provides code examples for developing, pretraining, and fine-tuning a Large Language Model (LLM) from the ground up. It serves as the official codebase for the book "Build a Large Language Model (From Scratch)." 📘

Notebook examples are included for each chapter:

Chapter 1: Understanding Large Language Models 🧠
Chapter 2: Working with Text Data 📝
Chapter 3: Coding Attention Mechanisms ⚙️
Chapter 4: Implementing a GPT Model from Scratch 🏗
Chapter 5: Pretraining on Unlabeled Data 📊
Chapter 6: Fine-tuning for Text Classification 🏷
Chapter 7: Fine-tuning to Follow Instructions 🗣

Repository: https://github.com/rasbt/LLMs-from-scratch 🔗

Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🚀 Fine-Tuning Large Language Models for Domain-Specific Tasks

Fine-tuning Large Language Models is the process by which generic LLMs are transformed into domain-specific experts. This procedure updates model weights using task-specific labeled data, rather than relying solely on prompting or retrieval mechanisms. This approach is particularly effective when language patterns remain stable and consistent outputs are required.

👉 Core Concept
A pre-trained LLM acquires general language capabilities. Fine-tuning instructs the model on how language functions within specific domains, such as healthcare, finance, legal services, or internal enterprise workflows.

👉 Practical Implementation
A customer support model is trained on thousands of instruction-response pairs. For example:
Input: Refund request for a delayed shipment
Output: A policy-compliant response including an apology, procedural steps, and a resolution.
Following fine-tuning, the model generates consistent, policy-aligned answers with lower latency compared to Retrieval-Augmented Generation (RAG).

👉 Significance of Parameter-Efficient Fine-Tuning
Techniques such as LoRA and QLoRA train only small adapter layers while keeping the base model frozen. This methodology reduces GPU memory consumption, accelerates training, and enables the fine-tuning of large models on hardware with limited resources.

👉 Appropriate Use Cases for Fine-Tuning
- Recurring domain-specific language
- Structured outputs, including classifications, summaries, or templates
- Stable knowledge bases that do not undergo daily changes
- Latency-sensitive systems where retrieval introduces overhead

Typical Production Stack
- Models: LLaMA or Mistral
- Frameworks: PyTorch with Hugging Face and PEFT
- Optimization: DeepSpeed or Accelerate
- Deployment: FastAPI, Docker, and cloud GPUs

💡 Fine-tuning enhances accuracy, consistency, and cost efficiency when applied to suitable problems.

A new open-source Python library titled "Fli" has been released, offering direct access to Google Flights. This library circumvents the web interface by interfacing directly with a reverse-engineered API to deliver rapid and structured results. The project is 100% open-source.

100% open-source.

The 10 Most Valuable AI Learning Repositories on GitHub 👇

I pulled the top 10 repos where Jupyter is the main language
Filtered for the best educational resources

Here's what's worth your time :

1. microsoft/generative-ai-for-beginners ⭐ 105,577 21
lessons covering the full GenAI stack From prompting basics to production deployment Built by Microsoft's AI education team
🔗 https://lnkd.in/diW9Cca6

2. rasbt/LLMs-from-scratch ⭐ 83,714
Build GPT-like models from zero No hand-waving, pure implementation Companion to Sebastian Raschka's book
🔗 https://lnkd.in/d3cq5diH

3. microsoft/ai-agents-for-beginners ⭐ 49,333
Complete course on agentic systems Covers planning, tools, memory, multi-agent Released 3 months ago, already essential
🔗 https://lnkd.in/e-a2gqSv

4. microsoft/ML-For-Beginners ⭐ 83,279
12 weeks of classical ML fundamentals 26 lessons, 52 quizzes, full curriculum Still relevant despite the LLM hype
🔗 https://lnkd.in/e7S8yDbS

5. openai/openai-cookbook ⭐ 71,106
Official OpenAI examples and guides Real production patterns, not toys Updated constantly with new features
🔗 https://lnkd.in/dtMbuMGk

6. jackfrued/Python-100-Days ⭐ 177,958
Most-starred educational repo on GitHub 100 days from Python beginner to advanced Covers web dev, data science, automation
🔗 https://lnkd.in/duWVtn4i

7. pathwaycom/llm-app ⭐ 54,583
Production RAG templates you can deploy Real-time data pipelines, not static demos Enterprise search with live updates
🔗 https://lnkd.in/daUFK9Nd

8. jakevdp/PythonDataScienceHandbook ⭐ 46,574
Entire data science handbook as Jupyter notebooks NumPy, Pandas, Matplotlib, Scikit-Learn Free alternative to $60 textbook
🔗 https://lnkd.in/db8HP7vT

9. CompVis/stable-diffusion ⭐ 72,246
Original Stable Diffusion implementation Understand how text-to-image actually works Foundation for SDXL, Midjourney competitors
🔗 https://lnkd.in/dEya2Rb5

10. facebookresearch/segment-anything ⭐ 53,250
Meta's SAM model for computer vision Promptable segmentation in images and videos Powers modern AI video editing tools
🔗 https://lnkd.in/dKvjk6Yb

📌 A comprehensive masterclass on Claude Code is available via this repository: https://github.com/luongnv89/claude-howto.

This resource provides a detailed visual and practical guide for one of the most powerful tools for developers. The repository includes:

• Step-by-step learning paths covering basic commands (/init, /plan) to advanced features such as MCP, hooks, and agents, achievable in approximately 11–13 hours. 📚
• An extensive library of custom commands designed for real-world tasks.
• Ready-made memory templates for both individual and team workflows.
• Instructions and scripts for:
- Automated code review.
- Style and standards compliance checks.
- API documentation generation.
• Automation cycles enabling autonomous operation of Claude without direct user intervention. ⚙️
• Integration with external tools, including GitHub and various APIs, presented with step-by-step guidance.
• Diagrams and charts to facilitate understanding, suitable for beginners. 📊
• Examples for configuring highly specialized sub-agents.
• Dedicated learning scripts, such as tools for generating educational books and materials to master specific topics efficiently.

Access the full guide here: https://github.com/luongnv89/claude-howto

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