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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
This repository contains Jupyter notebooks for the O'Reilly book "Transformers: The Definitive Guide."
It includes code for computer vision tasks, time series analysis, audio processing, and reinforcement learning.
https://github.com/Nicolepcx/transformers-the-definitive-guide
/channel/MachineLearning9 🤩
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You know the shape of the script before you open the editor. The hour goes to argparse, a retry wrapper, a rate limiter you have written eleven times already.
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▫️ describe a tool in a sentence and it writes, runs and returns the working script
▫️ ships a mini-app inside Telegram — a form, a converter, a dashboard, no deploy and no hosting
▫️ drop in a traceback or a repo link and get the fix, not a lecture
▫️ swap the model per task with one command, so cheap work runs cheap
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Roadmap for those who want to become a robotics engineer:
* Programming → Python, C++
* Mathematics → Linear algebra, calculus, probability theory
* Electronics → Sensors, motors, power systems
* Embedded systems → Microcontrollers, real-time operating systems, hardware interaction
* Control theory → PID controllers, modeling, stability
* Mechanics → Kinematics, dynamics, CAD systems
* Linux → Terminal, networking, debugging
* Robotics software → ROS 2
* Simulation → Gazebo, Isaac Sim
* Environmental perception → Computer vision, LiDAR, sensor data fusion
* Localization → Kalman filters, SLAM
Motion planning → A, RRT, trajectory generation
* Manipulator control → Inverse kinematics, object grasping
* AI for robotics → Reinforcement learning
* Building your own robots → Drones, rovers, robotic arms
* Autonomy → Perception → Planning → Control
* Deployment on real devices → Edge computing, AI directly on board
* Industrial robotics → PLCs, production automation
Matrix Calculus for Machine Learning and Beyond! — a free ebook from MIT.
This is the 2025 MIT textbook by Alan Edelman, Steven G. Johnson, and Paige Bright.
The book directly connects matrix calculus to modern machine learning.
Inside:
* Derivatives of matrices and vectors
* Jacobian and Hessian
* Matrix decompositions
* Optimization
* Differentiation in reverse mode
* Backpropagation of error
* Automatic differentiation
* Derivatives through ODEs
* Problems focused on machine learning
This is a comprehensive mathematical bridge between linear algebra, calculus, optimization, backpropagation, and machine learning.
Free ebook:
https://geni.us/Matrix-Calculus-Book
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🚨 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
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🔖 A comprehensive resource on LLMs in one repository
We found an open-source course covering Transformers, LoRA, RAG, prompts, model editing, and other key topics.
After each chapter, you can immediately access the original sources – the authors have compiled papers and collections from arXiv.
⛓Link to GitHub
https://github.com/ZJU-LLMs/Foundations-of-LLMs
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Tensor Algebra: A Small Concept That Has a Big Impact in AI 🧠
One thing I realized while learning deep learning is that tensors are everywhere. Whether you're working with TensorFlow, PyTorch, or building transformer models, almost everything revolves around tensor operations.
Although we often think of tensors as multi-dimensional arrays in machine learning, they're the structures that allow neural networks to efficiently represent and process complex data.
Here's a quick summary:
- Scalar (Rank 0): A single value
- Vector (Rank 1): A one-dimensional collection of values
- Matrix (Rank 2): A two-dimensional arrangement of values
- Tensor (Rank 3 or higher): A higher-dimensional representation used to model complex data
A few places where tensors show up every day:
- Images are represented as 3D tensors (Height × Width × Channels).
- Mini-batches become 4D tensors during model training.
- Transformer models process embeddings, attention scores, and hidden states as tensors throughout the network.
- Operations like matrix multiplication, broadcasting, reshaping, tensor contraction, and automatic differentiation power modern deep learning.
I created the infographic below as a simple visual reference while revisiting tensor algebra. I hope it's helpful for anyone learning deep learning or refreshing the fundamentals.
I'm curious. How did you first learn about tensors?
- Through mathematics?
- While using TensorFlow or PyTorch?
- During your first deep learning project?
- Or was there another resource that made the concept finally click?
I'd love to hear your experience and any resources you'd recommend for beginners. Looking forward to learning from your experiences and recommendations.
#DeepLearning #TensorFlow #PyTorch #AI #MachineLearning #Tensors
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📌Beyond-NanoGPT: Concise and annotated implementations of key deep learning ideas.
If you want to not just run pre-built models, but understand how they work "under the hood," the Beyond-NanoGPT repository is what you need. This project, created by a CS graduate student at Stanford University, serves as a bridge between simple examples like nanoGPT and complex implementations, offering dozens of implementations of modern deep learning methods.
Everything is written from scratch in PyTorch, with detailed comments – perfect for those who are tired of abstract papers and ruthless production code. Each line of code is written in a way that makes it clear how to use it in practice.
Stuck at the level of reading endless tutorials and want to move forward? This repository is a great step. It won't make you an expert in a week, but it will give you the tools to understand modern papers and start your own experiments. And yes, there's no fancy web interface or ready-made SaaS solutions here – just code, comments, and your curiosity. As it should be in research.
Getting started is very simple: clone the repository, install the dependencies, and you can start diving into the code. Architectures? There's a Vision Transformer for image classification, a Diffusion Transformer for generation, ResNet, and even an MLP-Mixer. Each script is a separate experiment.
For example, to train DiT on the CIFAR-10 dataset, you just need to run
train_dit.py
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Updated CS 8803 "Large Language Model" course at Georgia Tech for 2026.
The list of materials covers pre-training, Mixture of Experts (MoE), reasoning, reinforcement learning and self-play, agents, long context, scaling during inference, diffusion language models, safety, interpretability, and much more.
- https://cocoxu.github.io/CS8803-LLM-spring2026/
- https://docs.google.com/spreadsheets/d/1Oisf4imoNL3fs4UWGYAUlMCuYfCACMHCDb0iqEYU8wc/edit?usp=sharing
📚 "Natural Language Processing and Large Language Models" is a new open-access book from Springer, written by Chengqing Zong, Yang Zhao, and Yanjun Ma.
It's almost 400 pages long and provides an introduction to modern natural language processing and large language models.
Inside, you'll find information on: neural networks, distributed representations, language models, Transformers, BERT, GPT, tokenization, sentiment analysis, information extraction, text summarization, natural language understanding, machine translation, question answering, and RLHF.
In my opinion, this is a good reference guide for those who want to understand these topics without a very high barrier to entry. I would recommend it. ✨
https://link.springer.com/book/10.1007/978-981-92-0682-7
#NLP #LLM #ArtificialIntelligence #MachineLearning #DataScience #TechBooks
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Professor Steve Branton from the Mechanical Engineering Department at the University of Washington has uploaded a complete course on control theory for master's and doctoral students to YouTube. It's free.
The course is called Control Bootcamp.
It covers topics such as linear systems, stability and eigenvalues, controllability and observability, pole placement, the Kalman filter, LQR/LQG, robust control, and MPC – all explained sequentially with examples in Matlab.
Branton is the Boeing Professor of AI & Data-Driven Engineering at the University of Washington. He holds a bachelor's degree in mathematics from Caltech, with a specialization in control and dynamical systems, and a Ph.D. in mechanical and aerospace engineering from Princeton.
Playlist: https://youtube.com/playlist?list=PLMrJAkhIeNNR20Mz-VpzgfQs5zrYi085m
📚 This is probably one of the best technical books on how large language models are trained at scale:
> GPU memory and profiling
> Breaking down computations into blocks, kernel fusion, and FlashAttention
> Data parallelism, tensor parallelism, pipeline parallelism, and context parallelism
I've already read the free online version, but I still had to buy a physical copy for my library. 📖
You can also read it for free on Hugging Face:
https://huggingface.co/spaces/nanotron/ultrascale-playbook
#LLM #AI #MachineLearning #TechBooks #DataScience #Coding
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🔖Computer Science Fundamentals from MIT
We found the textbook Mathematics for Computer Science – covering the mathematics that underlies algorithms and computer science.
Logic, graphs, combinatorics, probability, induction, recurrence relations, and discrete structures – all in one place.
⛓️ Link to the textbook
https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-spring-2015/mit6_042js15_textbook.pdf
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"Linear Algebra with Applications" is a free and comprehensive textbook that introduces the computational, theoretical, and applied aspects of linear algebra.
The book covers topics such as systems of linear equations, matrices, determinants, vector spaces, linear transformations, eigenvalues and eigenvectors, diagonalization, inner product spaces, orthogonality, and many more. The explanations are accompanied by over 330 worked examples, exercises, and practical applications in geometry, electrical networks, dynamic systems, probability theory, and optimization.
A particularly interesting section discusses how Google's PageRank algorithm uses the dominant eigenvector to rank web pages. The links between websites are represented as a connectivity matrix, and the components of its dominant eigenvector provide an estimate of the relative importance of each page.
This is a very clear example of how an apparently abstract idea from linear algebra can underlie a real-world technology used on a massive scale.
The 2023 edition is available under a Creative Commons license. This is another excellent resource that is worth keeping as a reference.
https://collection.bccampus.ca/textbook/qTj4b4Ey
From machine learning and data visualization to time series and financial data.
This repository contains 920 open-source Python projects, categorized into 34 groups.
It's a great collection if you want to quickly find reliable libraries and tools for machine learning, data analysis, and related tasks, rather than searching everything manually on GitHub.
https://github.com/lukasmasuch/best-of-ml-python
🧲 Your agent writes the tool. You keep the terminal closed.
You know the shape of the script before you open the editor. The hour goes to argparse, a retry wrapper, a rate limiter you have written eleven times already.
Create your own AI agent inside Telegram in about a minute, and create small tools with it right in the chat.
▫️ describe a tool in a sentence and it writes, runs and returns the working script
▫️ ships a mini-app inside Telegram — a form, a converter, a dashboard, no deploy and no hosting
▫️ drop in a traceback or a repo link and get the fix, not a lecture
▫️ swap the model per task with one command, so cheap work runs cheap
▫️ remembers your stack, your conventions and your project for months
Setup takes a minute: open the link, start the free trial, name your agent.
30 days free, no card needed.
🧲 Create your agent
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Personal AI assistant in 5 minutes
No code. No card. Free 😳
Works in Telegram, WhatsApp, or Discord — just send it tasks by voice or text. It gets things done, not just tells you how to do them.
• reads and sends emails
• creates and edits Google Sheets
• uploads files to Google Drive
• works in Notion
• sends reminders
• generates PDFs, images, and videos
• actually makes life and work easier
🙌 If I only had one weekend to master Claude, I would start with these resources.
👩🏻💻 Stop saving dozens of different Claude guides that you'll never actually read! This list contains only the resources that are truly useful for real-world projects.
💗 Level 1 — Basic Fundamentals (17 minutes)
🟡 Claude Explained Simply (For Beginners)
🟡 Getting Started with Claude
➖ ➖ ➖
💗 Level 2 — Real-World Workflows (1 hour)
🟠 Working with Claude Daily
🟠 Claude for Work Teams
🟠 Brainstorming and Design with Claude
🟠 Combining Teamwork and Project Management
🟠 Creating Presentations with Claude
🟠 Claude Skills
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💗 Level 3 — Professional Level (3.5 hours)
🔵 How to Avoid Generic and Machine-Like Responses from Claude?
🔵 Coding with Claude
🔵 The Basics of Claude
🔵 How to Avoid Reaching Claude's Limit?
🔵 Saying Goodbye to Traditional Prompt Engineering
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💗 Level 6 — Expert Level (8 hours)
🟢 Understanding Claude's Computational Capabilities
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💡 Remember, you don't need dozens of different guides; you just need the right resources, in the right order.
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🤖Still Scared AI Is Coming For Your Job?
Here's 20 Repos To Actually Do Something About It
1. Prompt-Engineering-Guide
Explains how to actually get useful, reliable output from AI models instead of guessing and hoping.
2. AI-For-Beginners
Covers the broader AI landscape, not just LLMs, so you're not caught off guard by the next big shift either.
3. generative-ai-for-beginners
A calm, lesson-by-lesson intro to building with generative AI, no ML background assumed.
4. anthropic-cookbook
Real code examples for building things with Claude, straight from the people who make it.
5. openai-cookbook
The same idea for OpenAI's models, practical patterns instead of theory.
6. ML-For-Beginners
Twelve weeks of classic machine learning basics, useful groundwork before jumping into AI specifically.
7. llama_index
Handles connecting AI models to your own data and documents, a skill that comes up constantly in real jobs now.
8. ollama
Lets you run AI models on your own machine, good for understanding them without relying on an API key.
9. llama.cpp
Shows what's actually happening when a model runs, right down to the hardware level.
10. transformers
The library behind most modern AI models, worth exploring even just to see how they're built.
11. awesome-ai-agents
A running list of AI agent projects and frameworks, handy for keeping track of a space that moves fast.
12. nanoGPT
A small, readable implementation of GPT, built by Andrej Karpathy so you can see the whole thing without getting lost.
13. AutoGPT
One of the earliest autonomous AI agent projects, still useful for understanding how agents plan and act.
14. gpt-engineer
An AI agent that writes entire codebases from a prompt, worth trying just to see what it's actually capable of.
15. OpenHands
An open source AI coding agent, a good look at where AI-assisted development is heading.
16. awesome-machine-learning
A broad, well-organized list of ML resources and libraries across every language, a solid reference to keep bookmarked.
17. langchain
The most widely used framework for wiring AI models into actual applications, worth knowing even if you end up not using it.
18. Awesome-LLM
A curated list of papers, models, and tools, good for going deeper once the basics feel comfortable.
19. applied-ml
Real-world write-ups from companies on how they actually use ML and AI in production, not just in demos.
20. llm-course
A structured path from "what is an LLM" to actually fine-tuning and deploying one yourself.
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🔖 Machine Learning in Visualizations
On ML Visualized, you can literally observe how models are trained and how their behavior changes throughout the process.
This format greatly simplifies understanding of algorithms: less abstraction, more clarity.
http://ml-visualized.com/
Awesome Math is a comprehensive collection of math resources in a single repository.
It includes materials from Khan Academy, MIT OpenCourseWare, lecture notes, textbooks, and other free resources covering various areas of mathematics.
The project is active and quite popular, currently boasting over 16,000 stars on GitHub.
https://github.com/rossant/awesome-math