Forwarded from Machine Learning with Python
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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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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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