![Learn to build a Deep Learning library from scratch in Python and NumPy (autograd, CNNs, ResNets) [free]"](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-7756-300x171.png)
This project offers a comprehensive guide to building a deep learning library from scratch using Python and NumPy, aiming to demystify the complexities of modern frameworks. Key components include creating an autograd engine for automatic differentiation, constructing neural network modules with layers and activations, implementing optimizers like SGD and Adam, and developing a training loop for model persistence and dataset handling. Additionally, it covers the construction and training of Convolutional Neural Networks (CNNs), providing a conceptual and educational resource rather than a production-ready framework. Understanding these foundational elements is crucial for anyone looking to deepen their knowledge of deep learning and its underlying mechanics.
