Machine Learning with Python
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2 · 2.6K · Machine Learning with Python
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10 · 2K · Machine Learning with Python
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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.
What you will learn:
→ Time complexity and sorting.
→ Dynamic programming.
→ Advanced trees: segment tree, Fenwick tree, splay tree, link-cut tree.
→ Graph algorithms: traversals, shortest paths, flows.
→ String algorithms: substring search, suffix structures.
→ Fast Fourier Transform, linear programming, cryptography.
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20 · 2.1K · 链接
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731 · Machine Learning with Python
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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
and ONNX;
🫡 Distributed Training, FSDP, and Tensor Parallel;
🫡 Profiling and memory optimization;
🫡 Model serving and Ray.
A useful resource for both beginners and those already working with PyTorch in production.
⛓https://docs.pytorch.org/tutorials/
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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
Because of this addition, the attention mechanism or MLP within the layer – the function f in the formula above – actually does NOT transform the input. Instead, it calculates the information that needs to be ADDED to the input before passing it to the next block! ➕
Imagine the main part of the transformer as a shared whiteboard. 📝 Each block reads what it needs from it and adds its own notes. All changes are additions.
Furthermore, layers can exchange information over distances. A block in the first layer can write information, and a block in the fifth layer can read it, even though there is no direct connection between them. 🔗
I owe these ideas to an older article by Anthropic about the architecture of transformers:
transformer-circuits.pub/2021/framework
#Transformers #DeepLearning #AI #MachineLearning #NeuralNetworks #Tech
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5 · 1.4K · Machine Learning with Python
📚 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/
6. Data Science
https://t.co/TusyBWGfrx
7. Machine Learning
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8. Statistics
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9. Think Bayes
https://allendowney.github.io/ThinkBayes2/
10. Clean Code in Python — Packt
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57 · 2.1K · Machine Learning with Python
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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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🔖 A Comprehensive Resource for Machine Learning Practice
The platform offers:
🫡 1200+ machine learning tasks;
🫡 Mathematics, from linear algebra to PCA;
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Almost everything can be accessed directly in your browser, and basic tasks are available for free.
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7 · 443 · Machine Learning with Python
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🔖 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;
🫡 Projects using GPT, RL, and CUDA.
Almost everything can be accessed directly in your browser, and basic tasks are available for free.
⛓️ Link to the platform
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11 · 1.4K · 链接
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https://substack.com/@husseinsheikho?utm_source=share&utm_medium=android&r=3hwnr8
Follow me to more posts and useful content
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15 · 6.6K · Machine Learning with Python
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📚 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
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15 · 1.7K · 照片
Learn Deep Learning Cheat Sheet
@followers #DeepLearning #neuralnetworks #ArtificialIntelligence #machinelearning #ArtificialNeuralNetworks
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14 · 1.4K · 链接
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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?
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53 · 1.4K · Machine Learning with Python
If you want to get GOAT-level at software engineering,
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7 Developer roadmaps
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8 Path to senior engineer handbook
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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.
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❤6👍2
36 · 1.6K · Machine Learning with Python
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😰 15-Hour "Python for Data Science" Course
⚡️ Learn Python for Data Science
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4 · 778 · ⭐️ 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?
===
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32 · 1.1K · Machine Learning with Python
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💾 Free Deep Learning Course from NASA
We found a practical course on applying modern AI methods in astronomy and astrophysics.
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16 · 1.1K · Machine Learning with Python
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Oxford University has launched a free course on "Deep Learning Theory" for the 2026-2027 academic year.
The course focuses on the mathematical foundations of deep neural networks and includes 16 lectures that are freely accessible.
The program covers neural network architectures, nonlinear transformations, convolutional structures, pooling, optimization methods, dictionary learning, transferability of learned features, adversarial examples, random matrix theory, invariance learning, and other topics.
The lectures explore the mathematical theories that explain how deep neural networks learn and what accounts for their unique characteristics.
In my opinion, this is particularly valuable material for those who want to understand the mathematics that underlies modern AI systems.
https://courses.maths.ox.ac.uk/course/view.php?id=6621
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