Machine Learning

"Understanding Transformers and Attention Mechanisms" - a concise mathematical introduction to the attention mechanism, one of the key ideas in modern language models.

This document explains tokenization and embeddings, queries, keys, and values, attention scores and weights, multi-head attention, self-attention, causal attention and masking, cross-attention, and the basic structure of the Transformer architecture.

It also presents important techniques that make the attention mechanism in modern LLMs more efficient: KV-caching, grouped query attention (GQA), multi-query attention (MQA), and latent attention.

https://arxiv.org/pdf/2604.00965

Machine Learning pinned «https://t.me/UdemySybot?start=ref_418788114 Get Free Courses 😁»

📚 "Fundamentals of Computer Vision" is a free online book published by MIT Press, providing a broad introduction to computer vision from the perspectives of image processing and machine learning.

🔍 It covers topics such as image formation, training and backpropagation, image filtering and Fourier analysis, CNNs, RNNs, and transformers, generative models, representation learning, 3D geometry, motion estimation, object recognition, models that work with images and text, and much more.

💡 I particularly appreciate that the entire book is available directly in HTML, with a clear and user-friendly layout, and numerous diagrams and visualizations that help to understand the concepts.

🔗 https://visionbook.mit.edu/

#ComputerVision #MachineLearning #AI #TechBooks #MITPress #Education

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https://t.me/UdemySybot?start=ref_418788114

Get Free Courses 😁

Normalization vs Standardization 📊
Why they are not the same.

One page: the two formulas side by side, two columns from the same table on incompatible scales, the same 400 values shown raw / min-max / standardized so you can see only the location and scale move while the skew stays, a worked example on five numbers, and a "which one, when" guide.

The bottom line: ask what the next step assumes — a fixed interval means normalize, mean 0 and sd 1 means standardize, and if the model splits on ordering, neither.

#DataScience #MachineLearning #Statistics #Normalization #Standardization #DataPreprocessing

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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

https://t.me/MachineLearning9

"Learning Mathematics: Why Memory, Practice, and Technique are More Important than Talent"

To master mathematics, it is necessary to memorize a large amount of information, rules, methods of simplification, and problem-solving techniques. There is no other way. Theory is useful, but in moderation.

It's like learning a language. If you focus too much on grammar, you will never learn to speak it fluently.

https://algebrica.org/learning-mathematics/

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

"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 🤩

"Trigonometry" is a free, open-source textbook on trigonometry, with over 1000 pages, covering the subject from basic concepts to advanced topics.

The book covers angles and triangles, trigonometric relationships, the unit circle, sine, cosine, and tangent functions, graphs and their transformations, radians, solving triangles, the sine and cosine theorems, trigonometric identities and equations, inverse trigonometric functions, and formulas for the sum, difference, and double angle.

Later chapters also cover vectors, the dot product, polar coordinates, and complex numbers in polar form.

Each section contains numerous exercises, making the textbook particularly useful for reinforcing theoretical knowledge through practical application as you progress through the material.

https://louis.pressbooks.pub/trigonometry/

Directions for the development of hardware for deep learning – a lecture by Bill Dally at Georgia Tech, 2024.

youtu.be/gofI47kfD28

https://t.me/MachineLearning9

Pandas vs Polars — 14-section course cheatshee

https://t.me/MachineLearning9

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

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

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Cheat sheet for Scapy: creating and configuring packets, working with IP addresses, sending and receiving packets, sniffing traffic, fuzzing, and viewing packet structure. 📦🔍

#Scapy #Networking #Python #CyberSecurity #PacketCrafting #Hacking

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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

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 🫀

Machine Learning pinned «🤖 A Practical Tip for ML Data Collection When building a machine learning project, getting enough useful data is often just as important as the model itself. If you're collecting public web data for a dataset, you may need to access the same source from different…»

🤖 A Practical Tip for ML Data Collection
When building a machine learning project, getting enough useful data is often just as important as the model itself.
If you're collecting public web data for a dataset, you may need to access the same source from different locations or test how location affects the data returned.
A residential proxy can help with this by routing your requests through IPs from different regions.
For example, with Python:

import requests

proxies = {
    "http": "http://USER:PASSWORD@HOST:PORT",
    "https": "http://USER:PASSWORD@HOST:PORT"
}

response = requests.get(
    "https://example.com",
    proxies=proxies
)

print(response.status_code)

Replace USER, PASSWORD, HOST, and PORT with your proxy credentials.

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New users can use 711TRIAL to get 1GB of residential proxy traffic.
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