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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 you want to get GOAT-level at software engineering,
save & star these 10 GitHub repos:
1 System design academy
↳ https://github.com/systemdesign42/system-design-academy
2 Public APIs
↳ https://github.com/public-apis/public-apis
3 Tech interview handbook
↳ https://github.com/yangshun/tech-interview-handbook
4 Coding interview university
↳ https://github.com/jwasham/coding-interview-university
5 Engineering leadership
↳ https://github.com/gregorojstersek/resources-to-become-a-great-engineering-leader
6 Freecodecamp
↳ https://github.com/freeCodeCamp/freeCodeCamp
7 Developer roadmaps
↳ https://github.com/kamranahmedse/developer-roadmap
8 Path to senior engineer handbook
↳ https://github.com/jordan-cutler/path-to-senior-engineer-handbook
9 Free programming books
↳ https://github.com/EbookFoundation/free-programming-books
10 Build your own x
↳ https://github.com/codecrafters-io/build-your-own-x
Anything else you'd add?
♻ Repost to help others become better engineers.
/channel/DataAnalyticsX 🧑🏻💻
Learn Deep Learning Cheat Sheet
@followers #DeepLearning #neuralnetworks #ArtificialIntelligence #machinelearning #ArtificialNeuralNetworks
husseinsheikho?utm_source=share&utm_medium=android&r=3hwnr8" rel="nofollow">https://substack.com/@husseinsheikho?utm_source=share&utm_medium=android&r=3hwnr8
Follow me to more posts and useful content
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📚 Download 10 Python books for FREE:
1. Think Python — O’Reilly
https://t.co/X7y3pX68IW
2. The Big Book of Small Python Projects
https://inventwithpython.com/bigbookpython/
3. Data Structures
https://opendatastructures.org/ods-python.pdf
4. Data Science Handbook
https://t.co/9aOLdiAbim
5. Data Analysis
https://wesmckinney.com/book/
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https://t.co/TusyBWGfrx
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https://t.co/7veOwESe8q
8. Statistics
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9. Think Bayes
https://allendowney.github.io/ThinkBayes2/
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🔖 Official PyTorch Tutorials
This collection includes:
🫡 PyTorch fundamentals and torch.nn;
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torch.compile
Understanding algorithms changes the way you write code. 🧠 This playlist teaches you exactly that: understanding.
Pavel Mavrin. 61 lectures. Free. 🎓
Here's the difference:
Before understanding algorithms:
- You use hash tables for everything.
- You write the code first, then optimize it.
- You estimate the time complexity randomly.
After:
- You choose the appropriate data structure for the task.
- You think about complexity before writing the code.
- You learn an approach that is suitable for solving the problem.
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→ Time complexity and sorting.
→ Dynamic programming.
→ Advanced trees: segment tree, Fenwick tree, splay tree, link-cut tree.
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→ String algorithms: substring search, suffix structures.
→ Fast Fourier Transform, linear programming, cryptography.
https://www.youtube.com/playlist?list=PLrS21S1jm43igE57Ye_edwds_iL7ZOAG4
/channel/CodeProgrammer
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A collection of explanations for key concepts and scientific papers in machine learning:
https://github.com/dair-ai/ML-Papers-Explained
📖 "A Little Book on the Fundamentals of Generative AI" - an intuitive introduction to the mathematics:
arxiv.org/pdf/2605.29713
#GenerativeAI #Mathematics #DeepLearning #AIResearch #MachineLearning #arXiv
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If you're just starting to learn machine learning and want to delve deeper into the mathematics required for machine learning and deep learning, I recommend trying this platform. It's something like LeetCode for machine learning.
This is not an advertisement: I personally used it and decided to share it with you.
https://deep-ml.com
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We found a practical course on applying modern AI methods in astronomy and astrophysics.
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⛓ Link to the course
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⭐️ If you want to get dangerously good at generative AI,
then save these 10 GitHub repos:
1️⃣ Generative AI for Beginners
→ https://github.com/microsoft/generative-ai-for-beginners
2️⃣ LLM Course
→ https://github.com/mlabonne/llm-course
3️⃣ Build a Large Language Model From Scratch
→ https://github.com/rasbt/LLMs-from-scratch
4️⃣ Prompt Engineering Guide
→ https://github.com/dair-ai/Prompt-Engineering-Guide
5️⃣ Awesome LLM Apps
→ https://github.com/Shubhamsaboo/awesome-llm-apps
6️⃣ OpenAI Cookbook
→ https://github.com/openai/openai-cookbook
7️⃣ Hands-On Large Language Models
→ https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
8️⃣ GenAI Agents
→ https://github.com/NirDiamant/GenAI_Agents
9️⃣ Awesome Generative AI Guide
→ https://github.com/aishwaryanr/awesome-generative-ai-guide
🔟 Anthropic Courses
→ https://github.com/anthropics/courses
💬 Anything else you'd add to this list?
===
👋 PS - Want my AI Engineering Playbook for FREE?
Get a copy right now:
→ https://systemdesign42.github.io/ai-engineering-playbook/
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Agentic AI is the future.
If you want to become god-level with AI agents, save these 10 GitHub repos:
1 Awesome LLM Apps
🔗 https://github.com/Shubhamsaboo/awesome-llm-apps
2 Generative AI for Beginners
🔗 https://github.com/microsoft/generative-ai-for-beginners
3 LLM Course
🔗 https://github.com/mlabonne/llm-course
4 Prompt Engineering Guide
🔗 https://github.com/dair-ai/Prompt-Engineering-Guide
5 AI Agents for Beginners
🔗 https://github.com/microsoft/ai-agents-for-beginners
6 AI Agents Projects
🔗 https://github.com/ashishpatel26/500-AI-Agents-Projects
7 Hugging Face Agents Course
🔗 https://github.com/huggingface/agents-course
8 Awesome AI Agents
🔗 https://github.com/e2b-dev/awesome-ai-agents
9 GenAI Agents
🔗 https://github.com/NirDiamant/GenAI_Agents
10 Agents Towards Production
🔗 https://github.com/NirDiamant/agents-towards-production
Anything else you'd add?
📚 7 Best Websites to Learn Computer Science Subjects for Free
More: https://enghusseinsheikho.substack.com/p/7-best-websites-to-learn-computer?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
🔖 A Comprehensive Resource for Machine Learning Practice
The platform offers:
🫡 1200+ machine learning tasks;
🫡 Mathematics, from linear algebra to PCA;
🫡 Deep Learning, NLP, and Computer Vision;
🫡 Labs with real datasets;
🫡 Learning paths and interview preparation;
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Almost everything can be accessed directly in your browser, and basic tasks are available for free.
⛓️ Link to the platform
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"Graph Theory with Applications" is a free textbook that provides a comprehensive introduction to graph theory and its practical applications.
The book begins with basic concepts: graphs, subgraphs, paths, cycles, trees, and connectivity. It then moves on to more advanced topics: Eulerian and Hamiltonian graphs, matching, graph coloring, planar and directed graphs, network flows, and extremal graph theory.
What is particularly useful is that the authors combine a visual, geometric, and topological understanding of graphs with a rigorous presentation of the mathematical foundations.
This makes the book a valuable resource for studying the theory that underlies many modern applications of graphs in computer science, computer networks, optimization, data analysis, and related fields.
Book: https://book.huihoo.com/pdf/graph-theory-with-applications.pdf
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One of the key moments when I truly understood how transformers work: 🧠✨
"Stop thinking of a transformer as a conveyor belt, where each layer transforms the output of the previous one."
"Start thinking of it as a residual flow." 🌊
Each block in a transformer has a residual connection that adds the input of the block to its output:
x' = f(x) + x
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I found a great resource for interactive learning about machine learning and AI – VizLearn.
You can experiment with gradient descent, SVM, PCA, the Bayesian method, BPE tokenization, Q/K/V, KV-cache, quantization, and much more. You can change the input data and see how the calculations themselves change.
It's free and doesn't require registration.
https://vizlearn.in
/channel/MachineLearning9
"How to Train a Neural Network" is a concise summary of the MIT course lectures on deep learning from 2024. It focuses on one of the fundamental questions in neural networks: how a model learns its weights.
The summary examines the training process from a mathematical perspective. It covers topics such as forward propagation, loss functions, gradients, backpropagation, and gradient-based optimization methods.
I believe this is an interesting resource for those who want to go beyond a general, intuitive understanding of neural networks and begin to delve into the mathematics that underlies their training.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/mit6_7960_f24_lec2.pdf
/channel/CodeProgrammer 🤩
🧲 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
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Pandas vs Polars — 14-section course cheatshee
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This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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