Comments URL: https://news.ycombinator.com/item?id=47406602

Points: 2

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=47389887

Points: 2

# Comments: 0

[1] https://github.com/workofart/ml-by-hand

[2] https://github.com/workofart/ml-by-hand/blob/main/examples/c...

[3] https://github.com/workofart/ml-by-hand/blob/main/examples/g...

[4] https://www.henrypan.com/blog/2025-02-06-ml-by-hand/

[1] https://github.com/workofart/ml-by-hand

[1] https://github.com/workofart/ml-by-hand

[2] https://github.com/workofart/ml-by-hand/blob/main/examples/c...

[3] https://github.com/workofart/ml-by-hand/blob/main/examples/g...

I wanted to understand how models learn, like literally bridging the gap between mathematical formulas and high-level API calls. I feel like, as a beginner in machine learning, it's important to strip away the abstractions and understand how these libraries work from the ground up before leveraging these "high-level" libraries such as PyTorch and Tensorflow. Oh I also wrote a blog post [5] on the journey.

[1] https://github.com/workofart/ml-by-hand

[2] https://github.com/karpathy/micrograd

[3] https://github.com/workofart/ml-by-hand/blob/main/examples/c...

[4] https://github.com/workofart/ml-by-hand/blob/main/examples/g...

[5] https://www.henrypan.com/blog/2025-02-06-ml-by-hand/

I completely agree with this. Often, I think I understand something, but when I try to explain it to others, I quickly realize where my understanding is shaky. The gaps become even more apparent when I attempt to write it down because I have to structure my thoughts logically and precisely. Writing goes a step beyond speaking because it forces me to re-read and refine my ideas, whereas spoken words often disappear without deeper reflection. Oh, even this comment that I'm writing now was edited a couple of times before submitting it. The second half of the comment was added after re-reading the first half.

Comments URL: https://news.ycombinator.com/item?id=43141751

Points: 1

# Comments: 0

Key points:

- Everything is derived in code — no opaque black boxes.

- API mirrors PyTorch so you can pick it up quickly.

- You can train CNNs, RNNs, Transformers, and even GPT models.

- Designed more for learning/debugging than raw performance.

What’s different here?

While there are many powerful ML libraries available (TensorFlow, PyTorch, Scikit-learn, etc.), they often hide the underlying math behind layers of abstraction. I believe that to truly master these tools, you first need to understand how they work from the ground up. This project explicitly derives all the mathematical and calculus operations in the code, making it a hands-on resource for deepening the understanding of neural networks and library building :)

Check it out:

- Github Repository: https://github.com/workofart/ml-by-hand

- API Documentation: https://ml-by-hand.readthedocs.io/en/latest/

- Examples: Explore models like GPT-2, CNNs, Transformers, and LSTMs in the examples/ folder: https://github.com/workofart/ml-by-hand/tree/main/examples

- Blog Post: Read about the project’s motivation, design, and challenges at https://www.henrypan.com/blog/2025-02-06-ml-by-hand/

I’d love to hear any thoughts, questions, or suggestions — thanks for checking it out!

Comments URL: https://news.ycombinator.com/item?id=43020982

Points: 1

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=42988285

Points: 3

# Comments: 0

I built a machine learning library entirely from scratch using only Python and NumPy. I then used it to train a range of models—from classical CNNs, ResNets, RNNs, and LSTMs to modern Transformers and even a toy GPT-2. The motivation came from my curiosity about how to build deep learning models from scratch, like literally from mathematical formulas. I built this project not to replace production-ready libraries like PyTorch or TensorFlow, but to strip away the abstractions and reveal the underlying mathematics of machine learning.

Key points:

- Everything is derived in code — no opaque black boxes.

- API mirrors PyTorch so you can pick it up quickly.

- You can train CNNs, RNNs, Transformers, and even GPT models.

- Designed more for learning/debugging than raw performance.

What’s different here?

While there are many powerful ML libraries available (TensorFlow, PyTorch, Scikit-learn, etc.), they often hide the underlying math behind layers of abstraction. I believe that to truly master these tools, you first need to understand how they work from the ground up. This project explicitly derives all the mathematical and calculus operations in the code, making it a hands-on resource for deepening the understanding of neural networks and library building :)

Check it out:

- Github Repository: https://github.com/workofart/ml-by-hand

- API Documentation: https://ml-by-hand.readthedocs.io/en/latest/

- Examples: Explore models like GPT-2, CNNs, Transformers, and LSTMs in the examples/ folder: https://github.com/workofart/ml-by-hand/tree/main/examples

- Blog Post: Read about the project’s motivation, design, and challenges at https://www.henrypan.com/blog/2025-02-06-ml-by-hand/

I’d love to hear any thoughts, questions, or suggestions — thanks for checking it out!

Comments URL: https://news.ycombinator.com/item?id=42986598

Points: 1

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=18535360

Points: 2

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=10715359

Points: 1

# Comments: 0

Anything is appreciated, thanks!

Comments URL: https://news.ycombinator.com/item?id=10556812

Points: 4

# Comments: 1