NanoTorch
What is NanoTorch?
NanoTorch is a deep learning library inspired by the PyTorch framework, created using only Math and Numpy. It is designed for academic purposes, providing an educational resource for understanding the inner workings of neural networks.
Why Do We Need NanoTorch?
NanoTorch is created for educational purposes, aiming to demystify the complexities of neural networks and their efficient implementation.
The Purpose of NanoTorch
When learning about deep learning, we often encounter Neural Networks—mathematical abstractions of the human brain composed of linear layers and activation functions (non-linearities). These networks learn complex mathematical functions by analyzing large datasets.
To build these networks, we typically use well-designed frameworks like PyTorch from Meta or TensorFlow from Google. However, several questions arise:
- How do these neural networks work behind the scenes?
- How do frameworks implement them so efficiently?
Understanding the Implementation
While books explain the mathematics behind neural networks, their implementations often fall short of the efficiency and scalability seen in major frameworks. Understanding the source code of frameworks like PyTorch is challenging due to the additional layers of error handling and optimizations.
The Vision of NanoTorch
NanoTorch addresses this gap by providing a library with a PyTorch-like syntax and comprehensive documentation. This documentation not only explains the code but also delves into the mathematical concepts (e.g., gradients, matrices) and the process of transforming these equations into efficient and optimized code.
When we use NanoTorch
Note
NanoTorch is not recommended for real-world projects. It is not as powerful as PyTorch or TensorFlow and is intended solely for educational purposes.
By understanding how NanoTorch works, you will gain familiarity with the implementation principles of frameworks like PyTorch, providing a strong foundation for further exploration in the field of deep learning.