TL;DR
A freely available guide walks readers through implementing a minimal deep learning library starting from an empty file and NumPy. The project covers an autograd engine, layer modules and exercises that train MNIST, a simple CNN and a simple ResNet.
What happened
A step-by-step guide titled "Build a Simple Deep Learning Library" teaches readers how to construct a basic deep learning toolkit from first principles. The material begins with an empty file and relies on NumPy as the only dependency, then progresses until learners have implemented a working automatic differentiation (autograd) engine and a suite of layer modules. The guide includes hands-on training examples using the MNIST dataset and demonstrates model implementations for a simple convolutional neural network and a pared-down ResNet. The resource is published online at the provided URL and is free to read; the author offers an optional pay-what-you-want support channel on Gumroad and lists an email address for questions or feedback.
Why it matters
- Learning by building clarifies how high-level frameworks implement core features like autograd and layers.
- Working through a minimal implementation can demystify training workflows and model structure.
- A NumPy-only approach lowers tooling barriers for educational experimentation.
- Having concrete examples (MNIST, CNN, ResNet) helps bridge theory and practice for learners.
Key facts
- The guide starts from a blank file and uses NumPy as the base dependency.
- Readers implement a functional autograd engine and a collection of layer modules.
- Included training examples cover MNIST, a simple convolutional neural network, and a simple ResNet.
- The book or guide is free to read online at the linked URL.
- Support for the project is available via a pay-what-you-want option on Gumroad.
- A contact email (zekcrates@proton.me) is provided for questions or feedback.
- The resource is published at the specified URL and timestamped with the given publication date.
What to watch next
- Hands-on examples provided: training runs for MNIST, a basic CNN and a simple ResNet are included in the guide.
- not confirmed in the source: whether the codebase is actively maintained or receives regular updates.
- not confirmed in the source: availability of accompanying notebooks, prebuilt binaries, or packaged releases for easy setup.
Quick glossary
- NumPy: A foundational Python library for numerical computing, providing array objects and vectorized operations used in scientific code.
- Autograd: An automatic differentiation system that computes gradients of operations to enable gradient-based optimization.
- MNIST: A widely used dataset of handwritten digit images commonly used for benchmarking and educational examples.
- Convolutional Neural Network (CNN): A class of neural network architectures that applies convolutional layers, often used for processing image data.
- ResNet: A neural network architecture that uses residual connections to ease training of deeper models.
Reader FAQ
Is the guide free to access?
Yes — the resource is free to read online according to the source.
What will I build by following it?
The guide leads you to implement a basic autograd engine, layer modules, and train models such as MNIST, a simple CNN and a simple ResNet.
Can I get the source code or notebooks?
not confirmed in the source
How can I support the project?
Support is offered via a pay-what-you-want option on Gumroad as noted in the guide.
Build a Simple Deep Learning Library Preface Instead of just learning how to use a deep learning library, we are going to learn how to create one. We start with…
Sources
- Build a Deep Learning Library
- How I Built a Deep Learning Library from Scratch Using Only …
- Implementing a Deep Learning Library from Scratch in …
- DianCh/numpy-deep-learning
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