ML & Computer Science Fundamentals
From algorithms and data structures to machine learning, transformers, and modern deep learning.
Data Structures
Arrays, linked lists, stacks, queues, and hash tables with implementation details and time complexity analysis.
Sorting & Search
Complete quicksort visualizations, binary search, and algorithm complexity with step-by-step examples.
Mathematical Foundations
Linear algebra, calculus, and asymptotic analysis for machine learning optimization.
Machine Learning
Feature engineering, model evaluation, regularization, and classical algorithms.
Probability & Statistics
Bayes' rule, distributions, naive Bayes, and Markov processes for ML foundations.
Neural Networks
Deep learning fundamentals, backpropagation, and neural network architectures.
Attention & Transformers
Modern deep learning: attention mechanisms, self-attention, transformers, BERT, and GPT architectures.
Comprehensive Learning Journey
1. Mathematical Foundations
Start with linear algebra, calculus, and complexity theory to build the mathematical intuition needed for algorithms and ML.
2. Data Structures & Algorithms
Master fundamental data structures and sorting/search algorithms with detailed visualizations and MIT 6.006 integration.
3. Machine Learning Foundations
Apply your algorithmic knowledge to ML fundamentals, probability, and statistical learning theory.
4. Neural Networks & Deep Learning
Progress through classical neural networks, attention mechanisms, and transformers to modern architectures like BERT and GPT.
What Makes These Notes Special
Step-by-Step Visualizations
Detailed algorithm walkthroughs like quicksort partitioning with element-by-element moves
MIT 6.006 Integration
Systematic algorithmic foundations seamlessly integrated with practical implementations
Time Complexity Analysis
Complete asymptotic analysis and complexity theory connections throughout
Theory + Practice
Rigorous mathematical analysis combined with hands-on coding and LeetCode problems
Interconnected Learning
Cross-references and links between related topics across mathematics, algorithms, and ML
Modern Interface
Responsive design with LaTeX math rendering, searchable content, and intuitive navigation