# Under The Hood of Neural Networks - Am I Learning Enough

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# Neural Network Learning Resources

## Mathematics Fundamentals

### Linear Algebra

#### Books

- Linear Algebra and Its Applications - Gilbert Strang
- Linear Algebra Done Right - Sheldon Axler
- Matrix Analysis and Applied Linear Algebra - Carl D. Meyer

#### Video Courses

- MIT OpenCourseWare Linear Algebra
- 3Blue1Brown Essence of Linear Algebra

#### Research Papers

- "Fast Linear Algebra is Stable" - arXiv:1405.7903
- "Randomized Numerical Linear Algebra: Foundations & Algorithms" - arXiv:2002.01387

### Calculus

#### Books

- Calculus: Early Transcendentals - James Stewart
- Calculus Made Easy - Silvanus P. Thompson
- Advanced Calculus: A Differential Forms Approach - Harold M. Edwards

#### Video Courses

- MIT Single Variable Calculus
- 3Blue1Brown Essence of Calculus

#### Research Papers

- "The Calculus of Deep Learning" - arXiv:2102.01454

### Statistics & Probability

#### Books

- Introduction to Probability - Dimitri P. Bertsekas
- All of Statistics - Larry Wasserman
- Probability Theory: The Logic of Science - E. T. Jaynes

#### Video Courses

- Harvard Statistics 110
- MIT Probabilistic Systems Analysis

## Programming Fundamentals

### C Programming

#### Books

- Modern C - Jens Gustedt
- Expert C Programming: Deep C Secrets - Peter van der Linden
- C Programming: A Modern Approach - K.N. King
- 21st Century C - Ben Klemens

#### Online Resources

- C Programming Language Documentation
- GNU C Reference Manual
- C Programming Wikibook

## Neural Network Implementation

### Foundational Resources

#### Books

- Neural Networks and Deep Learning - Michael Nielsen
- Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville
- Pattern Recognition and Machine Learning - Christopher Bishop

#### Research Papers

- "Training Deep Nets with Sublinear Memory Cost" - arXiv:1604.06174
- "Deep Learning Hardware: Past, Present, and Future" - arXiv:1907.09423
- "Efficient BackProp" - Research Paper Link

### Advanced Implementation

#### Online Courses

- Stanford CS231n
- Fast.ai Deep Learning Course
- CMU Deep Learning Course

#### Code Repositories

- Tiny-DNN
- Neural Networks in C
- CNet - Neural Network in C
- NNPACK

#### Research Papers

- "Random Search for Hyper-Parameter Optimization" - Random Search for Hyper-Parameter Optimization
- "A Disciplined Approach to Neural Network Hyper-Parameters" - arXiv:1803.09820
- "An Analysis of Deep Neural Network Models for Practical Applications" - arXiv:1605.07678

## Performance Optimization

### System Level Optimization

#### Books

- High Performance Computing
- Optimizing C++
- Computer Systems: A Programmer's Perspective - Bryant and O'Hallaron

#### Research Papers

- "Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference" - arXiv:1712.05877
- "Mixed Precision Training" - arXiv:1710.03740

### Architecture Design

#### Books

- Clean Code
- The Art of UNIX Programming
- System Design Interview

#### Research Papers

- "A Hardware Architecture for Deep Learning" - arXiv:1908.07748
- "System Design for Large Scale Machine Learning" - Paper Link

## Additional Resources

### Developer Tools

- GDB Documentation
- Valgrind User Manual
- Perf Wiki

### Community Resources

- Papers With Code
- ArXiv Machine Learning
- Reddit r/MachineLearning

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