ML engineers, take note: structured ML reference guide

Link: https://ml-cheatsheet.readthedocs.io/en/latest/

There are no courses, no redundant theory, and no lengthy lectures here, but there are clear formulas, algorithms, the logic of ML pipelines, and a neatly structured knowledge base.

👉 @codeprogrammer

Trackers v2.1.0 has been released. In this release, support for ByteTrack has been added - a fast tracking-by-detection algorithm that maintains stable IDs even during occlusions.

Link: https://github.com/roboflow/trackers

pip install trackers

Trackers allows you to combine normal multi-object tracking with your detection or segmentation model.

👉 @codeprogrammer

GitHub has launched its learning platform: all #courses and certificates in one place.

#Git, #GitHub, #MCP, using #AI, #VSCode, and much more.

And most of the content is #free: → https://learn.github.com

👉 @codeprogrammer

Learn Python with the University of Helsinki

✓ With an official certificate
✓ From zero to advanced level
✓ 14 parts with practical tasks

All content is available → here
https://programming-25.mooc.fi/

👉 @codeprogrammer

Here's the full path I would recommend to build production-grade AI agents this year:

▪️a foundation in Python and algorithms
▪️mathematics and the basics of ML
▪️transformers and LLMs
▪️prompt engineering
▪️memory and RAG
▪️tools and integrations
▪️frameworks like LangChain or CrewAI
▪️multi-agent systems
▪️testing, deployment, and security

👉 @Codeprogrammer

🧠 Converting images to ASCII: text instead of pixels

Want to turn any image into ASCII art? It's not magic, just simple brightness processing.

It's tedious and stupid to do it manually
img = [
    [255, 0, 0],
    [0, 255, 0]
]
# Now we need to pick a symbol for each pixel...
# What a hassle.

Problem:
Manually selecting symbols by brightness is a pain. We need to automate the conversion of grayscale to symbols.

✔️ The right way (using gradation)

from PIL import Image

def image_to_ascii(path, width=100):
    img = Image.open(path)
    aspect = img.height / img.width
    height = int(width * aspect * 0.55)
    img = img.resize((width, height)).convert('L')

    ascii_chars = '@%#*+=-:. '
    pixels = img.getdata()

    ascii_art = '\n'.join(
        ascii_chars[pixel * (len(ascii_chars) - 1) // 255]
        for pixel in pixels
    )
    lines = [ascii_art[i:i+width] for i in range(0, len(ascii_art), width)]
    return '\n'.join(lines)

print(image_to_ascii('cat.jpg'))

How it works:
convert('L') converts the image to grayscale

Each pixel (0-255) is assigned a symbol from the set

The darker the pixel, the "denser" the symbol (e.g., '@'), the lighter - the "weaker" (space)

Let's write a converter with customizable palette:

class AsciiConverter:
    PALETTES = {
        'default&#39: '@%#*+=-:. ',
        'blocks&#39: '█rayed ',
        'detailed&#39: '$@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/\\|()1{}[]?-_+~<>i!lI;:,"^`\'. '
    }

    def __init__(self, palette_name='default&#39):
        if palette_name not in self.PALETTES:
            raise ValueError(f'Нет такой палитры, идиот. Выбери из: {list(self.PALETTES.keys())}')
        self.chars = self.PALETTES[palette_name]

    def convert(self, image_path, width=80):
        # ... code to convert using self.chars ...
        return ascii_result

Try specifying a non-existent palette - you'll get a clear error.

Key parameters:

🔵Width - determines the size of the final ASCII art
🔵Character palette - affects the detail and style
🔵Aspect ratio - important for correct display
🔵Inversion - you can invert the brightness for a dark background

Important:
ASCII art isn't just a fun thing. It's used to visualize data in the console, create creative logs, and even "hide" information in plain sight.

👩‍💻 @CodeProgrammer

#KMeans clustering animation in the style of 3blue1brown

👉 @CODEPROGRAMMER

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.iss.one/addlist/8_rRW2scgfRhOTc0

✅ https://t.iss.one/Codeprogrammer

9 key concepts of artificial intelligence, explained in 7 minutes

- Tokenization
- #TextDecoding
- #PromptEngineering
- Multi Step #AI Agents
- #RAGs
- #RLHF
- #VAE
- #DiffusionModels
- #LoRA

👉 @Python53

https://t.iss.one/RAICompass

مبادرة جميلة يرجى الانضمام اليها - للسوريين (مبادرة هامة) 🇸🇾

🐱 5 of the Best GitHub Repos
🔃 for Data Scientists

👨🏻‍💻 When I was just starting out and trying to get into the "data" field, I had no one to guide me, nor did I know what exactly I should study. To be honest, I was confused for months and felt lost.

▶️ But doing projects was like water on fire and helped me a lot to build my skills.

〰 Repo Awesome Data Analysis

🏷 A complete treasure trove of everything you need to start: SQL, Python, AI, data analysis, and more... In short, if you want to start from zero and strengthen your foundation, start here first.

                   ➖ ➖ ➖

〰 Repo Data Scientist Handbook

🏷 A concise handbook that tells you what you need to learn and what you can ignore for now.

                   ➖ ➖ ➖

〰 Repo Cookiecutter Data Science

🏷 A standard project template used by professionals. With this template, you can structure your data analysis and AI projects like a pro.

                   ➖ ➖ ➖

〰 Repo Data Science Cookie Cutter

🏷 This is also a very clean project template that teaches you how to build a data project that won’t fall apart tomorrow and can be easily updated. Meaning your projects will be useful in the real world from the start.

                   ➖ ➖ ➖

〰 Repo ML From Scratch

🏷 Here, the main AI algorithms are implemented from scratch in simple language. It’s great for understanding how models really work and for explaining them well in your interviews.

🌐 #Data_Science #DataScience