Artificial Intelligence, Machine Learning, and Deep Learning are not just buzzwords anymore — they are foundational technologies shaping the future of every industry. If you’re a student, data scientist, ML engineer, or even an AI-curious beginner, there’s one essential resource you must have in your arsenal:
👉 “All Cheat Sheets: Machine Learning, Deep Learning, Artificial Intelligence” — based on legendary course material from Stanford University and MIT.
It’s packed with formulas, definitions, diagrams, examples, and theorems — all on neatly structured pages that make revision lightning-fast and incredibly efficient.
Bookmark it. Print it. Study it. Share it.
It’s a rare gem in the world of open AI education.
And if you know other resources like this — I’d love to hear your recommendations.
👉 “All Cheat Sheets: Machine Learning, Deep Learning, Artificial Intelligence” — based on legendary course material from Stanford University and MIT.
🔍 What Is This PDF About?
This is not your average textbook. This PDF is a comprehensive and beautifully condensed cheat sheet that covers everything from:- 🧠 Supervised Learning
- 📊 Unsupervised Learning
- 🔍 Probability & Statistics
- 🧮 Linear Algebra & Calculus for ML
- 🤖 Deep Learning: CNNs, RNNs, Dropout, BatchNorm
- 🧩 Reinforcement Learning & Control
- 🧾 Evaluation Metrics for Classification & Regression
- 💡 Machine Learning Tips & Tricks
It’s packed with formulas, definitions, diagrams, examples, and theorems — all on neatly structured pages that make revision lightning-fast and incredibly efficient.
🎓 Why These Notes Stand Out
Here’s why I consider this one of the best AI notes resources available today:- University-Grade Quality: Compiled from Stanford’s CS229 and MIT’s Probability & Data courses, the content reflects academic rigor.
- Highly Visual: The notes contain tabular comparisons, diagrams, and clean formatting — no fluff, just clarity.
- Concise Yet Deep: Concepts like gradient descent, softmax, GDA, PCA, CLT, VC dimension, and Bayes’ Rule are explained with just the right amount of math and intuition.
- Saves Time: Whether you’re preparing for a coding interview, exam, or project, this PDF acts as your quick-reference guide.
🧵 Breakdown of the PDF Contents
✅ Section 1: Probability & Statistics
- Events, sample spaces, axioms
- Bayes’ Theorem, independence, expectation, variance
- Distributions: Bernoulli, Binomial, Geometric, Gaussian
- Central Limit Theorem, Markov & Chebyshev inequalities
✅ Section 2: Machine Learning (CS229)
- Linear Regression, Logistic Regression, SVM
- Generative vs Discriminative Models
- Naive Bayes, GDA, Decision Trees, Random Forests, Boosting
- k-NN, PCA, ICA, Model Selection
✅ Section 3: Deep Learning
- Neural networks architecture & backpropagation
- CNNs, BatchNorm, Dropout
- RNNs, LSTMs, Gates, Vanishing Gradients
- Activation functions, Gradient tricks
✅ Section 4: Reinforcement Learning
- MDPs, Policies, Bellman Equations
- Value iteration, Q-learning
- Policy optimization
✅ Section 5: Tips, Tricks & Evaluation
- Metrics like Accuracy, Precision, Recall, F1, AUC
- Regression metrics like R², AIC, BIC
- Regularization, Model diagnostics, Training techniques
📥 Download the PDF
This cheat sheet has gone viral for good reason — it’s the perfect AI quick reference.👉 Download the PDF here
✍️ Final Thoughts
AI is a vast and mathematically rich domain. Having a distilled, easy-to-reference, and academically sound document like this is priceless for:- Students preparing for university exams
- Engineers prepping for data science or ML interviews
- Professionals refreshing core concepts
- Self-learners navigating the complex world of AI
Bookmark it. Print it. Study it. Share it.
It’s a rare gem in the world of open AI education.
💬 Have You Used This Before?
Let me know in the comments or on social if this PDF has helped you too!And if you know other resources like this — I’d love to hear your recommendations.