A comprehensive, from-scratch learning resource covering algorithms, data structures, machine learning fundamentals, and modern transformer architectures — all in plain language.
Ever felt overwhelmed by scattered ML tutorials, confusing notation, and disconnected concepts? Bias to Action is different.
This is a complete, interconnected learning system that:
- Starts from first principles — No assumed knowledge
- Explains the "why" — Not just "what" or "how"
- Connects everything — Math → Algorithms → ML → Transformers
- Includes visualizations — LaTeX equations, algorithm diagrams, attention patterns
- Provides practical implementations — Python code with complexity analysis
- Covers cutting-edge topics — Attention mechanisms, BERT, GPT, modern transformers
Perfect for: Self-learners, bootcamp students, CS undergrads, ML engineers wanting to solidify fundamentals, and anyone preparing for technical interviews.
- 85+ markdown files spanning mathematics, algorithms, ML, and deep learning
- Cross-referenced content — Every topic links to related concepts
- Progressive difficulty — Builds from fundamentals to state-of-the-art
- MIT 6.006 Integration — Systematic algorithmic foundations
- Plain language explanations — No unnecessary jargon
- Step-by-step derivations — Follow the math from first principles
- Real-world examples — LeetCode problems, practical applications
- Complete Transformer Coverage — From attention basics to BERT vs GPT
- Mathematical Deep Dives — Query-Key-Value, scaled dot-product, multi-head attention
- Architecture Breakdowns — Encoder-decoder, positional encoding, layer normalization
- Contemporary Models — GPT-3/4, RoBERTa, InstructGPT, ChatGPT
- Modern UI — Responsive design with pastel theme
- LaTeX rendering — Perfect mathematical equations via MathJax
- Full-text search — Find anything instantly
- Mobile-friendly — Learn anywhere, anytime
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Attention Fundamentals • Self-Attention • Multi-Head Attention • Positional Encoding • Transformer Architecture • BERT vs GPT • Modern Variants (GPT-3/4, RoBERTa, ChatGPT) |
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Visit the live documentation — no setup required!
Perfect for note-taking and personal customization:
# Clone the repository
git clone https://github.com/Hanjie-Jiang/bias-to-action.git
cd bias-to-action
# Open _notes/ folder in Obsidian
# Download Obsidian: https://obsidian.md/Build and serve the documentation yourself:
# Clone the repository
git clone https://github.com/Hanjie-Jiang/bias-to-action.git
cd bias-to-action
# Install dependencies
pip install -r requirements.txt
# Serve locally
mkdocs serve
# Open http://127.0.0.1:8000/bias-to-action/I have designed a 20-week structured curriculum covering everything from fundamentals to transformers:
- Weeks 1-2: Mathematical Foundations (Linear Algebra, Calculus)
- Weeks 3-4: Data Structures (Arrays, Linked Lists, Stacks, Queues)
- Weeks 5-6: Algorithms (Search, Sort, Complexity Analysis)
- Weeks 7-8: Probability & Statistics
- Weeks 9-10: Classical ML (Regression, Decision Trees, KNN)
- Weeks 11-12: Attention Mechanisms & Self-Attention
- Weeks 13-14: Multi-Head Attention & Positional Encoding
- Weeks 15-16: Transformer Architecture (Encoder-Decoder)
- Weeks 17-18: BERT & GPT (Masked vs Causal Language Modeling)
- Weeks 19-20: Advanced Topics & Integration Project
| Feature | Description |
|---|---|
| Complete Coverage | 85+ files covering math → algorithms → ML → transformers |
| Interconnected | Every topic links to prerequisites and related concepts |
| Mathematical Rigor | LaTeX equations with step-by-step derivations |
| Practical Code | Python implementations with complexity analysis |
| LeetCode Integration | Real interview problems with detailed solutions |
| Visual Learning | Diagrams, attention visualizations, algorithm animations |
| Searchable | Full-text search across all content |
| Responsive | Works beautifully on desktop, tablet, and mobile |
Contributions are welcome and encouraged! Here's how you can help:
- Report bugs or suggest improvements via Issues
- Add new content — Fill in stub pages or expand existing topics
- Fix typos — Even small improvements matter
- Improve visualizations — Add diagrams or better examples
- Share — Star the repo and tell others!
See our Contributing Guide for details.
bias-to-action/
_notes/ # All markdown content
calculus_and_linear_algebra/ # Mathematical foundations
engineering_and_data_structure/ # Algorithms & data structures
ml_fundamentals/ # Classical ML
probability_and_markov/ # Statistics
neural_networks_and_deep_learning/ # Deep learning basics
attention_and_transformer_modern_neural_network/ # Transformers
assets/ # Images, CSS, resources
index.md # Landing page
mkdocs.yml # Site configuration
requirements.txt # Python dependencies
README.md # You are here!
Detailed structure: See Repository Structure below.
- Attention Mechanism — Query-Key-Value framework from scratch
- Self-Attention — Scaled dot-product with step-by-step math
- Multi-Head Attention — Why multiple heads? Parallel representation learning
- Positional Encoding — Sinusoidal vs learned embeddings
- Transformer Architecture — Full encoder-decoder breakdown
- BERT vs GPT — Bidirectional vs causal language modeling
- Modern Variants — GPT-3/4, InstructGPT, ChatGPT, RoBERTa
- Stacks & Queues — LIFO/FIFO operations with real-world applications
- Monotonic Stack — Next greater/smaller element patterns
- Quicksort Deep Dive — Partitioning visualizations step-by-step
- Binary Search Variations — Peak finding, rotated arrays
- 85+ Markdown Files — Comprehensive coverage
- 8 Major Topics — Math, algorithms, ML, transformers
- 20-Week Curriculum — Structured learning path
- 100% Open Source — Free forever
- Active Development — Regular updates and improvements
If you found this helpful:
- Star this repository to help others discover it
- Fork it to customize for your own learning
- Share it with friends, classmates, or study groups
- Provide feedback via Issues or Discussions
Every star motivates us to keep improving!
This project is licensed under the MIT License — see the LICENSE file for details.
Free to use, modify, and share. Just keep the attribution!
- ** Documentation:** hanjie-jiang.github.io/bias-to-action
- ** Learning Plan:** Foundational Knowledge Plan
- ** Issues:** GitHub Issues
- ** Discussions:** GitHub Discussions
Questions? Suggestions? Feel free to open an Issue or start a Discussion.
Build intuition, then build models.
Click to expand full directory structure
_notes/
index.md # Main landing page
Foundational knowledge plan.md # 20-week learning roadmap
Information_Theory.md # Information theory concepts
Integration_and_Project.md # Integration projects
assets/ # Website resources
images/ # General image resources
ml_fundamentals/ # ML fundamentals resources
styles/ # Custom CSS
hero.css # Hero section styling
layout.css # Main layout styling
calculus_and_linear_algebra/ # Mathematical Foundations
Calculus_and_Linear_Algebra_Overview.md
Linear_Algebra_for_ML.md # Vectors, matrices, operations
Calculus_and_Gradient_Descent.md # Optimization methods
Asymptotic_Analysis_Theory.md # Complexity theory
engineering_and_data_structure/ # Programming & Data Structures
Overview/
Engineering_and_Data_Structure_Overview.md
Data_Structures/
Arrays/
Arrays_Overview.md # Arrays vs linked lists
Dynamic_Arrays.md # Resizable arrays
Array_Problems.md # LeetCode problems
Linked_Lists/
Linked_Lists_Overview.md # Singly, doubly, circular
Linked_List_Implementation.md # Python implementation
Linked_List_Problems.md # Two pointers, reversal
Stacks/
Stacks_Overview.md # LIFO operations
Stack_Implementation.md # Array vs linked list
Stack_Problems.md # Valid parentheses, DFS
Queues/
Queues_Overview.md # FIFO operations
Queue_Implementation.md # Python deque
Queue_Problems.md # BFS, sliding window
Hash_Tables/
Hash_Tables_Overview.md
Hash_Functions_and_Collisions.md
Python_Dictionaries.md
Python_Dictionary_Operations.md
Python_Sets.md
Python_Set_Operations.md
Hash_Table_Problems.md
Recursion/
Recursion_Fundamentals.md
Recursive_Algorithms.md
Recursion_vs_Iteration.md
Common_Recursive_Patterns.md
Algorithms/
Search_Algorithms/
Search_Algorithms_Overview.md
Binary_Search_Fundamentals.md
Binary_Search_Variations.md
Search_Problems.md # Peak finding
Sorting_Algorithms/
Sorting_Algorithms_Overview.md # Quicksort deep dive
Sorting_Problems.md # LeetCode problems
Algorithmic_Patterns/
Two_Pointers/
Two_Pointers_Overview.md
Opposite_Direction_Pointers.md
Same_Direction_Pointers.md
Two_Pointers_Problems.md
Sliding_Window/
Sliding_Window_Overview.md
Fixed_Size_Window.md
Variable_Size_Window.md
Sliding_Window_Problems.md
Problem_Solving/
Set_Dictionary_Problems/
Array_Intersection.md
Non_Repeating_Elements.md
Unique_Elements.md
Anagram_Pairs.md
String_Problems/
Unique_Strings.md
Resources/
MIT_6006_Integration_Templates.md # MIT algorithm templates
Common_Patterns.md
Time_Complexity_Guide.md
Interview_Strategies.md
ml_fundamentals/ # Machine Learning Fundamentals
ML_Fundamentals_Overview.md
feature_engineering/
categorical_encoding.md
data_types_and_normalization.md
feature_crosses.md
model_evaluation/
evaluation_methods.md
metrics_and_validation.md
hyperparameter_tuning.md
regularization/
overfitting_underfitting.md
l1_l2_regularization.md
early_stopping.md
classical_algorithms/
linear_regression.md
logistic_regression.md
decision_trees.md
unsupervised_learning/
k_nearest_neighbors.md
k_means_clustering.md
language_model/ # Natural Language Processing
Ngram_Language_Modeling.md # N-gram models
resources/
Happy-LLM-v1.0.pdf # Reference materials
neural_networks_and_deep_learning/ # Deep Learning
Neural_Networks_and_Deep_Learning_Overview.md
neural_networks_sections/
Introduction_to_Perceptron_Algorithm.md
probability_and_markov/ # Probability & Statistics
Probability_and_Markov_Overview.md
probability_and_markov_sections/
conditional_probability_and_bayes_rule.md
joint_and_marginal_distributions.md
naive_bayes_and_gaussian_naive_bayes.md
resources/
conditional_probability.png
attention_and_transformer_modern_neural_network/ # Modern Deep Learning
Attention_and_Transformers_Overview.md # Complete overview
attention_fundamentals/
Attention_Mechanism_Overview.md # Q-K-V framework
Attention_Math.md # Scaled dot-product
self_attention/
Self_Attention_Overview.md # Self-attention mechanism
multi_head_attention/
Multi_Head_Attention_Overview.md # Multi-head attention
positional_encoding/
Positional_Encoding_Overview.md # Position embeddings
transformer_architecture/
Transformer_Architecture_Overview.md # Encoder-decoder
transformer_variants/
BERT_and_GPT_Overview.md # BERT vs GPT
javascripts/ # Website functionality
mathjax.js # LaTeX rendering
floating-nav.js # Navigation enhancements
View update history
- Attention & Transformers Section: Added comprehensive modern deep learning content covering attention mechanisms, transformers, BERT, and GPT
- Attention Fundamentals: Complete Query-Key-Value framework with scaled dot-product attention mathematics and intuitive explanations
- Self-Attention Mechanism: Detailed coverage of self-attention with step-by-step computations and practical applications
- Multi-Head Attention: Comprehensive overview explaining parallel attention heads, dimensionality, and representation subspaces
- Positional Encoding: Sinusoidal and learned position embeddings explaining how transformers capture sequential information
- Transformer Architecture: Full encoder-decoder architecture with feed-forward networks, layer normalization, and residual connections
- BERT vs GPT: Complete comparison of encoder-only (BERT, masked language modeling) vs decoder-only (GPT, causal language modeling) architectures
- Modern Variants: Coverage of RoBERTa, GPT-2/3/4, InstructGPT, ChatGPT, and contemporary transformer developments
- Navigation Integration: Updated front page hero section, sidebar navigation, and learning plan with Weeks 11-20 for transformer content
- Repository Cleanup: Added comprehensive .gitignore, removed build artifacts and OS metadata, cleaned nested directory structure
- Queue Implementation Mastery: Enhanced Queues Overview with comprehensive Python deque implementation and performance analysis
- FIFO vs LIFO Comparison: Added detailed comparison tables between queues, stacks, and priority queues with complexity analysis
- Data Structure Selection Guide: Added decision matrix for when to use queues vs other data structures with practical examples
- Monotonic Stack Problems: Added Daily Temperatures (LeetCode #739) with comprehensive solution using decreasing monotonic stack
- Stack Problem Collection: Enhanced Stack Problems section with detailed explanations of monotonic stack techniques
- Algorithm Optimization: Demonstrated how monotonic stacks improve from O(n²) brute force to O(n) optimal solutions
- Complete Data Structures Suite: Added comprehensive coverage of linked lists, stacks, and queues
- Linear Data Structures Mastery: Detailed theory, implementations, and problem patterns for LIFO and FIFO operations
- Navigation Integration: Updated MkDocs navigation, front page cards, and cross-references
- Sorting Algorithms Mastery: Completed comprehensive sorting section with detailed quicksort visualizations
- Search & Sort Integration: Finalized both search and sorting algorithms with theoretical depth
See CHANGELOG.md for complete history.