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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
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🙌 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
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💗 Level 2 — Real-World Workflows (1 hour)
🟠 Working with Claude Daily
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💗 Level 3 — Professional Level (3.5 hours)
🔵 How to Avoid Generic and Machine-Like Responses from Claude?
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🔵 The Basics of Claude
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🔵 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.
🤖 Claude 101
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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
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🧿ANTHROPH\C Launches 13 Free AI Courses
Anthropic Academy has announced 13 free AI courses. The courses cover the following topics:
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The courses are designed for a wide audience, from beginners to developers, educators, students, and business owners.
🔖 A comprehensive collection of resources on MLOps
We've found a useful collection for those who want to go beyond just training models and delve into their deployment and operation.
This repository contains the best materials on deploying, monitoring, automating ML pipelines, CI/CD, and other practices that are essential for production systems.
⛓ Link to GitHub
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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
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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.
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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.
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📚 "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. ✨
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"Introduction to Machine Learning" is another free textbook on machine learning, approximately 600 pages long, which emphasizes a deep mathematical understanding of the subject. 📚🧮
The book begins with the mathematical foundations necessary for further study: linear algebra, mathematical analysis, probability theory, matrix analysis, and optimization methods. It then covers the main supervised learning algorithms: linear and logistic regression, the k-nearest neighbors method, decision trees, random forests, boosting, and neural networks. 🤖📈
A significant portion of the book is dedicated to probabilistic and generative models. It discusses Monte Carlo methods, graphical models, Bayesian networks, variational methods, normalizing flows, variational autoencoders (VAEs), and generative adversarial networks (GANs). 🎲🧠
The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. 🔍📊
In my opinion, this is an excellent resource for those who want to gain a broad understanding of machine learning and understand the mathematics underlying the key methods, rather than treating them as "black boxes." 💡✨
https://arxiv.org/pdf/2409.02668
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