Machine Learning with Python (@CodeProgrammer) — Telegram channel posts

11 September 2026

Machine Learning with Python

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5 · 3.2K · 04:23

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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

11 · 2.7K · 06:07

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wam-sq-en-codeprogrammer.mp4 · 1.8 MB · click to show

🧲 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 — 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 — 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. 30 days free, no card needed. 🧲 Create your agent

3 · 3.3K · 16:08

12 September 2026

Machine Learning with Python

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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.me/MachineLearning9 🤩

4 · 2.3K · 05:38

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🎓 Deep Learning for Text with PyTorch: NLP & Transformers #Data_Science #DataCamp 🏫 Platform: DataCamp 🆓 100% FREE ━━━━━━━━━━━━━━━━━━━━ 📝 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: https://t.me/Courses27

5 · 3K · 08:37

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Python for Data Science Cheat Sheet.pdf · 372 KB · click to show

😰 "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. ⬅️ 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.me/CodeProgrammer

35 · 3.2K · 08:51

13 September 2026

Machine Learning with Python

33 · 3.5K · 05:23

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2 · 3.3K · 17:31

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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

15 · 3.7K · 20:02

15 September 2026

Machine Learning with Python

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67 · 6.1K · 05:42

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3 · 4.6K · 19:50

17 September 2026

Machine Learning with Python

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30 · 8.6K · 05:32

18 September 2026

Machine Learning with Python

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5 · 4.7K · 06:02

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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}") Without it, you would have to retrieve the value separately using input(), and then check it. Have you ever used the := operator in your code? #Python #Programming #WalrusOperator #Coding #TechFacts #Python3 ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

7 · 4.8K · 16:15

24 September 2026

Machine Learning with Python

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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/ https://t.me/MachineLearning9 🫀

32 · 3.2K · 07:45

26 September 2026

Machine Learning with Python

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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 https://t.me/MachineLearning9

16 · 2.9K · 06:23

27 September 2026

Machine Learning with Python

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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 ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

9 · 1.8K · 05:54

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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 https://t.me/CodeProgrammer

57 · 7.8K · 06:12

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pandas_vs_polars_cheatsheet.png · 1.1 MB · click to show

Pandas vs Polars — 14-section course cheatshee https://t.me/MachineLearning9

9 · 2.9K · 09:06

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📖 "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 ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

24 · 4K · 17:22

29 September 2026

Machine Learning with Python

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wam-sq-en-vasilisa-dev-hussein.mp4 · 3.8 MB · click to show

🧲 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 — 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 — describe the task on the way home, read the result when you are back Setup takes a minute: open the link and name your agent. Free to use — no card needed. 🧲 Try your own agent

2 · 4K · 15:21

1 October 2026

Machine Learning with Python

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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

25 · 3.6K · 06:55

3 October 2026

Machine Learning with Python

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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 https://t.me/CodeProgrammer 🤩

32 · 4.8K · 16:09

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2 · 1.6K · 21:30