"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://xn--r1a.website/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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"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

Hands On Python Data Science - Data Science Bootcamp

Master Python for Data Science with Real-World Applications: Dive Deep into Data Analysis, Machine Learning

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💎 By: https://xn--r1a.website/Udemy26
#Programming #Coding #Development #Tech #Python #DataScience

100 Machine Learning Interview Questions and Answers 🤖

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A distinctive bot for subscribing to courses on the most popular educational platforms across multiple fields, most notably programming, artificial intelligence, mechanics, medicine, and other sciences. 🎓🤖

This bot provides a free subscription to any course available within the bot. 🆓

https://xn--r1a.website/UdemySybot?start=ref_418788114

#FreeCourses #Programming #AI #Education #Udemy #TelegramBots

Tracking experiments and versioning ML models with MLflow. 🤖

Machine learning development requires saving hyperparameters, metrics, and artifacts for each run to compare results. The MLflow platform logs key metrics and registers trained models in a central registry. We will install MLflow, run a script to log parameters, and register the model.

Let's install the

mlflow

and

scikit-learn

libraries to conduct and log a test experiment. 📦

pip install mlflow scikit-learn

The dependencies for managing ML experiments have been successfully installed. ✅

Now, let's create a Python script called

train.py

that will train a simple model, log metrics, and save it to MLflow. 🐍

import mlflow
from sklearn.ensemble import RandomForestClassifier

mlflow.set_experiment("demo_experiment")
with mlflow.start_run():
    params = {"n_estimators": 100, "max_depth": 5}
    mlflow.log_params(params)
    model = RandomForestClassifier(**params)
    mlflow.log_metric("accuracy", 0.95)
    mlflow.sklearn.log_model(model, "rf_model")

The training script and metric logging are set up and ready to be executed. 🚀

Let's run the training script to capture the results and parameters in the local MLflow storage.

python3 train.py

The experiment has been successfully completed, and the parameters and model artifacts have been saved. 📊

# verification (check for registered runs in MLflow)
mlflow runs list --experiment-name demo_experiment

Expected output:

demo_experiment ... FINISHED

# cleanup (remove the generated directory with artifacts and the script)
rm -rf mlruns train.py

Using MLflow helps avoid chaos when tuning hyperparameters and ensures reproducibility of results. Deploy an MLflow server on a separate host for the entire team to collaborate on the project. 🌐

#MLflow #MachineLearning #Python #DataScience #MLOps #AI

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