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

Pandas vs Polars — 14-section course cheatshee

https://t.me/MachineLearning9

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

"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/

"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/

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

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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📚 "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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