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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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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Day 7 of self-studying Berkeley CS189 — stochastic gradient descent notes 📚📝
🔥 *Stochastic Gradient Descent (SGD)* is a powerful optimization algorithm used to minimize loss functions in machine learning. Unlike batch gradient descent, which uses the entire dataset to compute gradients, SGD updates parameters using a single training example (or a small mini-batch) at a time.
🚀 Key Benefits:
- Faster convergence on large datasets
- Escapes local minima more easily
- Suitable for online learning scenarios
📊 The Update Rule:
Where
📌 Challenges:
- High variance in updates
- Requires careful tuning of the learning rate
🧠 *Tip:* Use momentum or adaptive learning rates (like Adam) to stabilize training!
#MachineLearning #CS189 #SGD #DeepLearning #DataScience #Algorithms
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🔥 *Stochastic Gradient Descent (SGD)* is a powerful optimization algorithm used to minimize loss functions in machine learning. Unlike batch gradient descent, which uses the entire dataset to compute gradients, SGD updates parameters using a single training example (or a small mini-batch) at a time.
🚀 Key Benefits:
- Faster convergence on large datasets
- Escapes local minima more easily
- Suitable for online learning scenarios
📊 The Update Rule:
θ = θ - α * ∇J(θ; x⁽ⁱ⁾, y⁽ⁱ⁾)Where
α is the learning rate and (x⁽ⁱ⁾, y⁽ⁱ⁾) is a single training example.📌 Challenges:
- High variance in updates
- Requires careful tuning of the learning rate
🧠 *Tip:* Use momentum or adaptive learning rates (like Adam) to stabilize training!
#MachineLearning #CS189 #SGD #DeepLearning #DataScience #Algorithms
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🔖 Learning Data Science through interactive examples
One of the most useful repositories for those who want to better understand machine learning.
It transforms complex concepts into visual experiments: you can study models, change parameters, and immediately see the results.
⛓ Link to GitHub
https://github.com/GeostatsGuy/DataScienceInteractivePython
#DataScience #MachineLearning #Python #Learning #Tech #GitHub
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One of the most useful repositories for those who want to better understand machine learning.
It transforms complex concepts into visual experiments: you can study models, change parameters, and immediately see the results.
⛓ Link to GitHub
https://github.com/GeostatsGuy/DataScienceInteractivePython
#DataScience #MachineLearning #Python #Learning #Tech #GitHub
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🔖 Over 300 real-world case studies of ML systems from top companies. 🤖
We found a repository that collects genuine ML engineering experience – not theory from textbooks, but real stories of implementing models in production. 📚
Inside, you'll find case studies from Uber, Netflix, Google, and other companies: how they built the architecture, what problems arose, where the systems failed, and what solutions helped them recover. 🏗️
⛓ Link to GitHub
https://github.com/Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies
#MachineLearning #MLCaseStudies #DataScience #Engineering #Uber #Netflix
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We found a repository that collects genuine ML engineering experience – not theory from textbooks, but real stories of implementing models in production. 📚
Inside, you'll find case studies from Uber, Netflix, Google, and other companies: how they built the architecture, what problems arose, where the systems failed, and what solutions helped them recover. 🏗️
⛓ Link to GitHub
https://github.com/Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies
#MachineLearning #MLCaseStudies #DataScience #Engineering #Uber #Netflix
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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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🔗 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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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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