🔖 The Big Book on Fine-Tuning LLMs

A free 115-page book dedicated to the retraining of large language models. 📚

It's suitable for those who want to understand how to prepare datasets, configure training, and improve the quality of LLMs for their tasks. 🚀

#LLM #FineTuning #AI #MachineLearning #DataScience #Tech

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

5 Fun Papers That Explain LLMs Clearly 📚✨

Want to understand LLMs better? Start with these five foundational papers that explain how they work. 🤖

Large language models (LLMs) can feel complicated at first. There are transformers, attention layers, scaling laws, pretraining, instruction tuning, human feedback, retrieval, and many other ideas around them. 🧠 But the best way to understand large language models is not to start with a huge textbook. A better way is to read a few important papers that each explain one major part of the system. 📄 This article is part of a fun series where we learn by exploring core ideas, practical projects, and the research papers behind modern technology. 🔬 In this article, we will go through five papers that explain how LLMs work. So, let's get started. 🚀

More: https://www.kdnuggets.com/5-fun-papers-that-explain-llms-clearly

#LLM #AI #MachineLearning #DeepLearning #DataScience #Tech

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

10 GitHub repositories that are worth checking out for an AI engineer 🤖

1. Hands-On AI Engineering 🛠️

A collection of AI applications and agent systems with practical use cases of LLM.

👉 https://github.com/Sumanth077/Hands-On-AI-Engineering

2. Hands-On Large Language Models 📘

Full code from the book Hands-On Large Language Models: from basics to fine-tuning.

👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models

3. AI Agents for Beginners 🎓

A free course from Microsoft with 11 lessons on creating AI agents.

👉 https://github.com/microsoft/ai-agents-for-beginners

4. GenAI Agents 🤖

A large collection of tutorials and implementations of agent systems.

👉 https://github.com/NirDiamant/GenAI_Agents

5. Made With ML 🚀

About the development, deployment, and support of production-ready ML systems.

👉 https://github.com/GokuMohandas/Made-With-ML

6. Learn Harness Engineering ⚙️

A practical course on Harness Engineering for AI agents.

👉 https://github.com/walkinglabs/learn-harness-engineering

7. AutoResearch 🔬

Autonomous cycles of ML experiments from Andrej Karpathy.

👉 https://github.com/karpathy/autoresearch

8. Designing Machine Learning Systems 📚

Notes and materials from Chip Huyen's book.

👉 https://github.com/chiphuyen/dmls-book

9. Awesome LLM Inference ⚡

A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.

👉 https://github.com/xlite-dev/Awesome-LLM-Inference

10. LLM Course 🗺️

A practical course on LLM with a roadmap and Colab notebooks.

👉 https://github.com/mlabonne/llm-course

#AI #MachineLearning #LLM #DataScience #Tech #GitHub

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

Learn AI for free directly from top companies. 🚀

1 - Anthropic:
anthropic.skilljar.com

2 - Google:
grow.google/ai

3 - Meta:
ai.meta.com/resources/

4 - NVIDIA:
developer.nvidia.com/cuda

5 - Microsoft:
learn.microsoft.com/en-us/training/

6 - OpenAI:
academy.openai.com

7 - IBM:
skillsbuild.org

8 - AWS:
skillbuilder.aws

9 - DeepLearning.AI:
deeplearning.ai

10 - Hugging Face:
huggingface.co/learn

💬 Comment "Learning" if you find this helpful.

🔄 Repost so others can take help.

🔖 Must bookmark for future reference.

#AI #MachineLearning #Tech #FreeLearning #DataScience #AIForAll
https://t.me/CodeProgrammer

Python Programming for Beginners: Learn Python from Scratch

Python Programming for Beginners: Learn Python from Scratch (Master Data Analysis, Step-by-Step with Practice Exercises)…

🏷 Category: development
🌍 Language: English (US)
👥 Students: 38,796 students
⭐️ Rating: 4.3/5.0 (964 reviews)
🏃‍♂️ Enrollments Left: 9
⏳ Expires In: 0D:4H:4M
💰 Price: $9.59 ⟹ FREE
🆔 Coupon: 02E367A95E4BACD13ECE

⚠️ Watch 2 short ads to unlock your free access.

💎 By: https://t.me/Udemy26
#Programming #Coding #Development #Tech #Python #DataScience

My new hobby: searching for beautiful projects on GitHub and being inspired by their design. 🚀✨

https://www.gitsearchai.com/ 🔗

#GitHub #OpenSource #Tech #Programming #Design #Inspiration

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🔖 Comprehensive Practical Course on Reinforcement Learning

We've found a repository that will help you learn Reinforcement Learning, from basic concepts to advanced algorithms.

The author supports the theory with practical examples using TensorFlow, making the material ideal for self-study.

⛓️ Link to GitHub
https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow

#ReinforcementLearning #TensorFlow #MachineLearning #DeepLearning #AI #Tech

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Google just released a free 2-hour course on full Graph engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 🤖⚙️

10% → 17:44 - build your first agent 🚀
30% → 39:30 - Loop engineering: iterate, check, break 🔁
60% → 1:12:38 - Graph engineering 🕸️
75% → 1:34:26 - agents that throttle themselves ⚡
100% → 1:55:05 - full graph for multi-agentic systems 🏗️

everyone builds one agent and calls it done - this is the full system where agents wire themselves into a graph.

watch the course, build the graph - then read the full architecture below ↓

More: https://telegra.ph/Graph-Engineering-build-1000-agent-loops-in-one-window-from-one-prompt-full-5-step-course-08-02

#GraphEngineering #AI #Agents #Graphs #Tech #Coding

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DS FULL ARCHIVE 🗂

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#DS #Archive #DataScience #Tech #News #Update

🚨 Cambridge has just released a real bombshell this time.

📚 A whole collection of classic textbooks on AI and machine learning is now available for free in PDF format.

If you want to really understand machine learning and don't want to waste money on overpriced courses, these ten books will be enough to build a very solid foundation.

From simple to complex.

1️⃣ Understanding Machine Learning

One of the best books for beginners. It covers the basic theoretical algorithms of machine learning.

🔗 https://cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf

2️⃣ Mathematical Foundations of Machine Learning

If you're not very confident in your math skills, I would start here.

🔗 https://mml-book.github.io/book/mml-book.pdf

3️⃣ Mathematical Analysis of Machine Learning Algorithms

A more in-depth look at the mathematical principles of machine learning algorithms.

🔗 https://tongzhang-ml.org/lt-book/lt-book.pdf

4️⃣ Theoretical Principles of Deep Learning

The theoretical foundations of deep learning and an understanding of why it all works.

🔗 https://arxiv.org/pdf/2106.10165

5️⃣ Neural Networks and Learning Machines

A systematic analysis of neural networks and the principles of their training.

🔗 https://arxiv.org/pdf/1901.05639

6️⃣ Graph Deep Learning

A good starting point for those who want to understand graph neural networks.

🔗 https://yaoma24.github.io/dlg_book/dlg_book.pdf

7️⃣ Machine Learning: A Probabilistic Perspective

It allows you to look at machine learning from a probabilistic and algorithmic perspective.

🔗 https://people.csail.mit.edu/moitra/docs/bookexv2.pdf

8️⃣ Probability Theory: Theory and Examples

Fundamental theory of probability. Very useful if you want to understand machine learning beyond the level of using ready-made libraries.

🔗 https://sites.math.duke.edu/~rtd/PTE/PTE5_011119.pdf

9️⃣ Fundamentals of Applied Probability

More focus on the practical application of probability theory.

🔗 https://sites.math.duke.edu/~rtd/EP4A/EP4A_April2021.pdf

🔟 Advanced Data Analysis

An advanced level for those who want to seriously improve their data analysis skills.

🔗 https://stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf

#AI #MachineLearning #FreeBooks #DataScience #DeepLearning #Tech

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