🔖 A large collection of lectures on Machine Learning and Deep Learning 🧠

We found a repository that brings together high-quality materials on several areas of artificial intelligence. 🤖

Excellent material for both learning and reviewing key topics. 📚

⛓️ Link to GitHub
https://github.com/kmario23/deep-learning-drizzle

#MachineLearning #DeepLearning #AI #Tech #Coding #Learning

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Maths, CS & AI Compendium: A free textbook for aspiring AI/ML engineers

🚀 A large open-source compendium on mathematics, computer science, and AI has gone viral on GitHub. The project already has around 6.3K stars.

📚 The author positions it as a "non-traditional textbook" for practitioners: less dry notation, more intuition, connections between topics, and real-world context.

📖 It contains 20 chapters:
* Vectors, matrices, calculus
* Statistics and probability
* Machine learning and deep learning
* NLP, computer vision, audio/speech
* Multimodal learning and autonomous systems
* GNN, OS, algorithms
* Production engineering, GPU/SIMD
* AI inference, ML systems design, and applied AI

🤖 There is also a MCP server so that Claude Code, Cursor, VS Code, and other AI assistants can use the compendium as a local knowledge base.

💡 This is a great resource for those who want to not just "learn ML," but to build a solid foundation: mathematics → CS → ML systems → modern AI.

🔗 GitHub: https://github.com/HenryNdubuaku/maths-cs-ai-compendium

#AI #MachineLearning #ComputerScience #Maths #OpenSource #DevCommunity

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sequence of four inputs, carrying every hidden state forward yourself. 🔄

1. Given

Four inputs X1 to X4, recurrent weights and biases for hidden layers a, b, c, and an output layer y. 📊

2. Initialize

Let us set the hidden states a0, b0, c0 to zeros. Nothing has been read yet. 🛑

3. First hidden layer (a)

We build the transformation matrix by laying the input weights, the state weights and the biases side by side. We stack X1, the previous state a0, and an extra 1 underneath. Multiply the two, and a1 = [0, 1]. 🧮

4. Second hidden layer (b)

Let us do it again, one layer up. Now a1 is the input, and b0 is the previous state. Multiply: b1 = [1, -1]. ⬆️

5. Third hidden layer (c)

Once more. b1 is the input, c0 is the previous state, and c1 = [1, 1]. 🔁

6. Output layer (y)

Let us read the answer off the top of the stack. Weights and biases against [c1; 1], and Y1 = [3, 0, 3]. 📝

7. Carry the states forward

We copy a1, b1, c1 across. This is the whole trick of a recurrent network: the states are the only thing the next input gets to see. 🚀

8. Process X2

Repeat steps 3 to 6 for the second input: three hidden layers, then the output. Y2 = [5, 0, 4]. 🔢

9. Carry the states forward

Let us copy a2, b2, c2 across, exactly as before. 🔄

10. Process X3

Same four moves, third input. Y3 = [13, -1, 9]. 🧩

11. Carry the states forward

We copy a3, b3, c3 across, one last time. ⏭️

12. Process X4

Repeat once more. Y4 = [15, 7, 2]. ✅

You have just run a Deep RNN over a whole sequence by hand. ✍️

The outputs:
Y1: [3, 0, 3]
Y2: [5, 0, 4]
Y3: [13, -1, 9]
Y4: [15, 7, 2]

The takeaway: the hidden states are the memory, and they are the only memory there is. Everything the network learns from X1 has to fit in those little two-cell columns and get handed forward, one step at a time. 🧠

#RNN #DeepLearning #AI #MachineLearning #NeuralNetworks #Tech

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I kept running into the same problem: some of the best AI/ML books are legally free. The authors put them up on their own sites, but the links are scattered across personal pages, university sites, and random GitHub repos nobody finds.

So I built a single index: Awesome Free AI Books. 30+ books across Deep Learning, Reinforcement Learning, Bayesian/Probabilistic ML, NLP & LLMs, Math for ML, Computer Vision, Generative Models, Causal Inference, GNNs, and AI Safety. Think Goodfellow’s Deep Learning, Sutton & Barto’s RL bible, Murphy’s Probabilistic ML, Bishop’s latest, Jurafsky & Martin’s SLP3 draft, and more.

Every link points straight to the author’s or publisher’s own page—no rehosted PDFs, no shady mirrors. A weekly GitHub Action checks all links so they don't rot over time. 🔄

It’s open source and open to contributions. If you know a legitimately free book that’s missing, PRs and issues are welcome. 🤝

Repo:
https://github.com/MarcosSete/awesome-free-ai-books

#AI #MachineLearning #DeepLearning #NLP #LLMs #OpenSource

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Attention Heatmap vs Token Pruning 🔍✂️

🔗 More: https://www.overshoot.ai/blogs/an-introduction-to-token-pruning-for-vlms

#AI #MachineLearning #TokenPruning #DeepLearning #TechNews #VLM

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🚀 TOP 8 Machine Learning Regression Metrics Explained

Choosing the right metric isn't academic; it's the difference between a model that works in production and one that breaks trust.

Here's the map every ML engineer should carry in 2026:

1️⃣ MEAN ABSOLUTE ERROR (MAE)
Average miss, easy to explain. On average, we're off by 5 units.

2️⃣ MEAN SQUARED ERROR (MSE)
Squares mistakes → big errors hurt more.

3️⃣ ROOT MEAN SQUARED ERROR (RMSE)
Square root of MSE. Same unit as the target, easier to relate.

4️⃣ R² COEFFICIENT
Explains how much variation your model captures. But don't confuse fit with usefulness.

5️⃣ ADJUSTED R²
Keeps R² honest. Extra useless features won't inflate the score.

6️⃣ MAPE (Mean Absolute Percentage Error)
Errors in percentages. Great for business dashboards, weak if actual values get near zero.

7️⃣ Huber Loss
Blends MAE & MSE. Punishes small errors like MSE, resists outliers like MAE.

8️⃣ Quantile Loss
Perfect when predicting ranges instead of single points like demand at the 90th percentile.

👁 VIEW

● = Actuals ○ = Predictions

MAE → avg |●-○|
MSE → avg (●-○)²
RMSE → √MSE
R² → variance explained
MAPE → % error
Huber → balance (MSE + MAE)
Quant → percentile accuracy

🏆 THE TAKEAWAY
Metrics decide what success looks like.
Choose wrong, and your good model is useless.
Choose right, and you build trust, adoption, and impact.

📝 TL;DR
MAE → simple error
MSE → punishes big errors
RMSE → interpretable scale
R² → fit, not prediction power
Adj R² → guards against overfitting
MAPE → % view, fragile near zero
Huber → outlier-resistant
Quantile → forecasts ranges

#MachineLearning #DataScience #RegressionMetrics #MLOps #AI #TechTips

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🔖 5 Free Courses on AI Agents

1. https://huggingface.co/learn/agents-course — AI Agents Course 🤗

2. https://deeplearning.ai/courses/ai-agents-in-langgraph — AI Agents in LangGraph 🧠

3. https://deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/ — Multi AI Agent Systems with CrewAI 🤖

4. https://microsoft.github.io/AI-For-Beginners/agentic-ai/ — AI Agents for Beginners 🚀

5. https://deeplearning.ai/courses/building-code-agents-with-hugging-face-smolagents — Building Code Agents with Hugging Face smolagents 💻

If you want to learn about Agentic AI, save this collection. 💾

#AI #ArtificialIntelligence #MachineLearning #TechNews #FreeCourses #LearnAI

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"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

#MachineLearning #DeepLearning #AI #Mathematics #DataScience #NeuralNetworks

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🔥 Land Your Dream Job – Free Interview Prep Resources Inside!

🌈Struggling with tough interview questions? Nervous about technical grilling? You're not alone.

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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

#MachineLearning #Python #AI #DataScience #MLTools #Programming

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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

#AI #MachineLearning #FreeBooks #DataScience #DeepLearning #Tech

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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

#LLM #AI #MachineLearning #TechBooks #DataScience #Coding

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🌈 2026 Job-Seeker Toolkit – Free Interview & IT Cert Resources

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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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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. 🆓

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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. 🌐

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

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🔖 A Comprehensive Resource for Machine Learning Practice

The platform offers:

🫡 1200+ machine learning tasks;
🫡 Mathematics, from linear algebra to PCA;
🫡 Deep Learning, NLP, and Computer Vision;
🫡 Labs with real datasets;
🫡 Learning paths and interview preparation;
🫡 Projects using GPT, RL, and CUDA.

Almost everything can be accessed directly in your browser, and basic tasks are available for free.

⛓️ Link to the platform
https://enghusseinsheikho.substack.com/p/a-comprehensive-resource-for-machine?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

#MachineLearning #DeepLearning #DataScience #AI #Coding #Tech

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