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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
🔖 A Comprehensive Resource for Machine Learning Practice
The platform offers:
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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
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"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
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Pandas vs Polars — 14-section course cheatshee
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OpenAI researcher Alice Liu went through 57 interviews before being hired, and then openly shared her entire preparation and job search journey.
If you're preparing for Research Scientist or MTS positions, this is one of the most comprehensive resources available.
You can use her notes directly, or simply use the list of topics to study them yourself. This kind of information is rarely published.
Notes on LLMs:
https://alisawuffles.notion.site/alisa-s-book-of-llms
Mathematics:
https://alisawuffles.notion.site/math-notes
Analysis of the job search and interview process:
https://alisawuffles.github.io/blog/job-search/
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📚 7 Best Websites to Learn Computer Science Subjects for Free
Struggling with subjects like DSA, DBMS, OS or Computer Networks? These free resources can make learning much easier
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• Excellent introduction to computer science
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4. Neso Academy 📖
• Detailed lectures on OS, CN, DBMS, DSA and other CSE subjects
• Great for concept-based learning
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• University-level computer science courses
• Lectures, assignments and study material
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7. roadmap.sh 🗺
• Structured roadmaps for different tech careers
• Useful for planning what to learn next
🔗 https://roadmap.sh/
💾 Save this list for your next semester
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😰 "Python for Data Science" Cheat Sheet
👨🏻💻 This file is a "comprehensive cheat sheet" for data scientists. Whenever you forget how to join data or customize a chart while coding, just refer to it.
⬅️ Chapter 1: All NumPy functions for creating arrays and broadcasting.
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📚 Download 10 Python books for FREE:
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2. The Big Book of Small Python Projects
https://inventwithpython.com/bigbookpython/
3. Data Structures
https://opendatastructures.org/ods-python.pdf
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🔖 Official PyTorch Tutorials
This collection includes:
🫡 PyTorch fundamentals and torch.nn;
🫡 Computer Vision and transfer learning;
🫡 NLP and RNNs;
🫡 Reinforcement Learning;
🫡
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.
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→ Graph algorithms: traversals, shortest paths, flows.
→ String algorithms: substring search, suffix structures.
→ Fast Fourier Transform, linear programming, cryptography.
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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
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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.
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Uniface
Automate face detection, recognition, and analysis of key facial landmarks with the Uniface Python library.
https://github.com/yakhyo/uniface
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Here's a small fact about Python 🐍
The := operator is called the "walrus" because the symbols resemble the eyes and tusks of a walrus 🦭
It was introduced in Python 3.8 and allows you to assign a value to a variable and use it directly within the expression at the same time.
For example:
while (line := input("Say something: ")) != "quit":
print(f"You said: {line}")input(), and then check it.:= operator in your code?
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If you fully understand this article, you will understand inference better than 90% of people.
And by the way, this is just the first material in the AI Performance Engineering repository.
It's scary to think how much knowledge is contained in the rest.
https://github.com/wafer-ai/gpu-perf-engineering-resources
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🎓 Deep Learning for Text with PyTorch: NLP & Transformers
#Data_Science
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🏫 Platform: DataCamp
🆓 100% FREE
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📝 Course Details:
Natural Language Processing has undergone massive advancements, and this advanced PyTorch course takes learners through the evolution of text modeling. Moving from standard tokenization and RNNs to modern Transformer architectures and attention mechanisms, it delivers a comprehensive blueprint for deep NLP application design.
Who It's For
Advanced machine learning engineers and NLP specialists who want to master PyTorch text preprocessing, recurrent networks, Transformers, and transfer learning.
Key Takeaways
• Text Preprocessing & Encodings: Master tokenization, stemming, lemmatization, One-Hot, Bag-of-Words, and TF-IDF encodings for neural networks.
…
📢 Channel: /channel/Courses27