nnfs io

Finding a free resource that matches the rigor of *Neural Networks from Scratch* (NNFS.io) can be tricky because Harrison Kinsley (Sentdex) put a massive amount of detail into that book. However, there are several "gold standard" free alternatives that cover the same ground—some even in **Rust**.

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## 1. The "NNFS" Community Repository (Multi-Language)

Since you specifically mentioned wanting different languages like Rust, the best place to start is actually the **[NNfSiX GitHub Repository](https://github.com/Sentdex/NNfSiX)**.

* **What it is:** This is the community-driven extension of the NNFS book. It contains the code for the book's projects (dot products, layers, activation functions, backpropagation) translated into dozens of languages.
* **Languages:** Includes **Rust**, C++, Go, Java, and even Assembly.
* **Best use:** Use this alongside the [Sentdex YouTube Playlist](https://www.google.com/search?q=https://www.youtube.com/playlist%3Flist%3DPLQVvvaa0QuDcjD5BAw2DxE6mB2uk7XdXl) (which is free) to see how the logic translates into Rust syntax.

## 2. Neural Networks and Deep Learning (Online Book)

By Michael Nielsen, this is widely considered the best free "from scratch" resource in existence.

* **Link:** [neuralnetworksanddeeplearning.com](http://neuralnetworksanddeeplearning.com/)
* **Style:** It focuses on the **math and theory** first, then implements it in Python (though the logic is easy to port to Rust).
* **Why it's like NNFS:** It doesn't use libraries. It forces you to write the backpropagation and stochastic gradient descent logic yourself.

## 3. "CrabNet" & Rust Tutorials

If you want to stick strictly to the **Rust** ecosystem, there are several high-quality, step-by-step guides that mirror the NNFS "scratch" philosophy:

* **[Building a Neural Network in Rust (From Scratch)](https://dev.to/farshed/building-a-neural-network-in-rust-from-scratch-5bm1):** A great introductory post that goes through the `struct` setup for neurons and layers without external ML crates.
* **[Tutorial: How to Build a Simple Neural Network in Rust](https://www.juliushietala.com/blog/crabnet-tutorial):** Focuses on the "software engineer" perspective, bridging the gap between math and memory-safe Rust code.
* **[Creating a Neural Network from Scratch in Rust (Series)](https://medium.com/@jesuskevin254/creating-a-neural-network-from-scratch-in-rust-part-1-f9f8d30ed75b):** A multi-part series on Medium that starts with the Perceptron and moves into multi-layer architectures.

## 4. Andrej Karpathy’s "micrograd"

While in Python, this is arguably the most "from scratch" you can get.

* **Link:** [micrograd GitHub](https://github.com/karpathy/micrograd) / [Video Lecture](https://www.youtube.com/watch?v=VMj-3S1tku0)
* **The Hook:** He builds a tiny "autograd" engine. Once you understand `micrograd`, you can rewrite it in Rust (which is actually a very popular project for Rust learners because of how Rust handles the computation graph/ownership).

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### Comparison at a Glance

| Resource | Language | Level | Focus |
| --- | --- | --- | --- |
| **NNfSiX (GitHub)** | Rust, C++, etc. | Beginner | Direct translation of NNFS code. |
| **Michael Nielsen** | Python/Math | Intermediate | Deep theoretical understanding. |
| **Karpathy (micrograd)** | Python | Intermediate | Understanding Automatic Differentiation. |
| **Farshed (Dev.to)** | Rust | Beginner | Syntax-specific implementation in Rust. |

**Would you like me to find a specific Rust implementation of a backpropagation engine so you can see how the ownership model handles the gradients?**