Machine Learning with Python
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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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πŸ”–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

https://t.iss.one/CodeProgrammer β€οΈπŸ”°
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Forwarded from Machine Learning
πŸ“š 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. πŸ“–

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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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
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🧲 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 β€” 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
▫️ voice in, answer back β€” describe the task on the way home, read the result when you are back

Setup takes a minute: open the link, start the free trial, name your agent.
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🧲 Create your agent
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Forwarded from Machine Learning
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

https://t.iss.one/MachineLearning9 🀩
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Forwarded from Free Online Courses
πŸŽ“ Deep Learning for Text with PyTorch: NLP & Transformers

#Data_Science
#DataCamp

🏫 Platform: DataCamp
πŸ†“ 100% FREE

━━━━━━━━━━━━━━━━━━━━
πŸ“ 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
β€’ Text Preprocessing & Encodings: Master tokenization, stemming, lemmatization, One-Hot, Bag-of-Words, and TF-IDF encodings for neural networks.
…

πŸ“’ Channel: https://t.iss.one/Courses27
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Python for Data Science Cheat Sheet.pdf
372.3 KB
😰 "Python for Data Science" Cheat Sheet

πŸ‘¨πŸ»β€πŸ’» 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.

⬅️ Chapter 2: Everything about Pandas, from selecting rows and columns (loc/iloc) to handling time series.

⬅️ Chapter 3: A complete catalog of charts (scatter plots, bar charts, histograms, pie charts).

⬅️ Chapter 4: The golden section! A summary table listing all the important commands in one place.


https://t.iss.one/CodeProgrammer
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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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πŸ“š 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

1. GeeksforGeeks πŸ§‘β€πŸ’»
β€’ DSA, DBMS, OS, CN and more
β€’ Notes, articles, problems and tutorials
πŸ”— https://www.geeksforgeeks.org/

2. freeCodeCamp πŸŽ“
β€’ Programming, web development, Python and more
β€’ Free courses with hands-on learning
πŸ”— https://www.freecodecamp.org/

3. CS50 by Harvard 🧠
β€’ Excellent introduction to computer science
β€’ Covers programming, algorithms, memory, databases and more
πŸ”— https://cs50.harvard.edu/

4. Neso Academy πŸ“–
β€’ Detailed lectures on OS, CN, DBMS, DSA and other CSE subjects
β€’ Great for concept-based learning
πŸ”— https://www.youtube.com/@nesoacademy

5. W3Schools 🌐
β€’ Learn HTML, CSS, JavaScript, SQL, Python and more
β€’ Simple explanations with interactive examples
πŸ”— https://www.w3schools.com/

6. MIT OpenCourseWare πŸŽ“
β€’ University-level computer science courses
β€’ Lectures, assignments and study material
πŸ”— https://ocw.mit.edu/

7. roadmap.sh πŸ—Ί
β€’ Structured roadmaps for different tech careers
β€’ Useful for planning what to learn next
πŸ”— https://roadmap.sh/

πŸ’Ύ Save this list for your next semester
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