Learning Machine Learning

My ongoing journey for learning Artificial Intelligence / Machine Learning using PyTorch and NumPy.

Technologies

Why PyTorch and NumPy?

Implementing in first PyTroch allows someone to understand at a basic level how a model works, making implementing it in NumPy easier due to having a structured path for implementation.

What have I done?

To start I implemented an estimation of sin⁡(x) using SGD1 and a simple Neural Network in PyTroch. Then after understanding the code and overall overview of how my approximation of sin⁡(x) achieved in PyTroch I implemented the same model purely in NumPy. After solving sin⁡(x) I moved onto MNIST2, which I completed in PyTroch.

Future plans for this project?

I am planning to complete MNIST2 in NumPy and then move on to CIFAR-103, Object classification4 in stream, LLM's5, and autoregressive models6.

Repository: Github

  1. Stochastic gradient descent.
    w:=w−η∇f(x) ↩

  2. The MNIST database of handwritten digits has a training set of 60,000 examples and a test set of 10,000 examples. ↩↩

  3. The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. ↩

  4. Object detection is the task of identifying an object in an image. ↩

  5. Large Language Models. ↩

  6. I am referencing OpenAI's autoregressive model based image generation. ↩