"Introduction to Machine Learning" is another free textbook on machine learning, approximately 600 pages long, which emphasizes a deep mathematical understanding of the subject. 📚🧮

The book begins with the mathematical foundations necessary for further study: linear algebra, mathematical analysis, probability theory, matrix analysis, and optimization methods. It then covers the main supervised learning algorithms: linear and logistic regression, the k-nearest neighbors method, decision trees, random forests, boosting, and neural networks. 🤖📈

A significant portion of the book is dedicated to probabilistic and generative models. It discusses Monte Carlo methods, graphical models, Bayesian networks, variational methods, normalizing flows, variational autoencoders (VAEs), and generative adversarial networks (GANs). 🎲🧠

The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. 🔍📊

In my opinion, this is an excellent resource for those who want to gain a broad understanding of machine learning and understand the mathematics underlying the key methods, rather than treating them as "black boxes." 💡✨

https://arxiv.org/pdf/2409.02668

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A Collection of Machine Learning Libraries for Python 🤖

A large repository containing over 900 libraries and frameworks for machine learning. 📚

All projects are sorted by quality and popularity, which helps you quickly find the best tools for working with AI and ML. ⚙️

Repo: https://github.com/ml-tooling/best-of-ml-python?tab=readme-ov-file#vector-similarity-search-ann

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🚨 Cambridge has just released a real bombshell this time.

📚 A whole collection of classic textbooks on AI and machine learning is now available for free in PDF format.

If you want to really understand machine learning and don't want to waste money on overpriced courses, these ten books will be enough to build a very solid foundation.

From simple to complex.

1️⃣ Understanding Machine Learning

One of the best books for beginners. It covers the basic theoretical algorithms of machine learning.

🔗 https://cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf

2️⃣ Mathematical Foundations of Machine Learning

If you're not very confident in your math skills, I would start here.

🔗 https://mml-book.github.io/book/mml-book.pdf

3️⃣ Mathematical Analysis of Machine Learning Algorithms

A more in-depth look at the mathematical principles of machine learning algorithms.

🔗 https://tongzhang-ml.org/lt-book/lt-book.pdf

4️⃣ Theoretical Principles of Deep Learning

The theoretical foundations of deep learning and an understanding of why it all works.

🔗 https://arxiv.org/pdf/2106.10165

5️⃣ Neural Networks and Learning Machines

A systematic analysis of neural networks and the principles of their training.

🔗 https://arxiv.org/pdf/1901.05639

6️⃣ Graph Deep Learning

A good starting point for those who want to understand graph neural networks.

🔗 https://yaoma24.github.io/dlg_book/dlg_book.pdf

7️⃣ Machine Learning: A Probabilistic Perspective

It allows you to look at machine learning from a probabilistic and algorithmic perspective.

🔗 https://people.csail.mit.edu/moitra/docs/bookexv2.pdf

8️⃣ Probability Theory: Theory and Examples

Fundamental theory of probability. Very useful if you want to understand machine learning beyond the level of using ready-made libraries.

🔗 https://sites.math.duke.edu/~rtd/PTE/PTE5_011119.pdf

9️⃣ Fundamentals of Applied Probability

More focus on the practical application of probability theory.

🔗 https://sites.math.duke.edu/~rtd/EP4A/EP4A_April2021.pdf

🔟 Advanced Data Analysis

An advanced level for those who want to seriously improve their data analysis skills.

🔗 https://stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf

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📚 This is probably one of the best technical books on how large language models are trained at scale:

> GPU memory and profiling
> Breaking down computations into blocks, kernel fusion, and FlashAttention
> Data parallelism, tensor parallelism, pipeline parallelism, and context parallelism

I've already read the free online version, but I still had to buy a physical copy for my library. 📖

You can also read it for free on Hugging Face:

https://huggingface.co/spaces/nanotron/ultrascale-playbook

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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/

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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. 🌐

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