/fromthetensor

From the Tensor to Transformers, a rough outline for a course.

From the Tensor to Stable Diffusion

Inspired by From the Transistor.

Machine learning is hard, a lot of tutorials are hard to follow, and it's hard to understand software 2.0 from first principles.

You wanna be an ML engineer? There's the steps to get good at that:

  1. Download a paper
  2. Implement it
  3. Keep doing this until you have skills

-- George Hotz

Section 1: Intro: Cheating our way past the Tensor -- 0.5 weeks

So about those Tensors -- Course overview. Describe how Deep Learning models are buildable using Tensors and how different architectures like CNNs and RNNs use Tensors in different ways. Understand the concept of backpropagation and gradient descent. [video]

Accelerated learning -- Training on a personal computer limits the reach of this course. Using something like Google Colab will allow anyone with a computer to play.

Section 2: Deep Learning: What is deep learning anyway? -- 0.5 weeks

Building a simple Neural Network -- Your first little program! Getting the model working. Learning the basics of deep learning. [code] [video]

Building a simple CNN -- An intro chapter to deep learning, learn how to build a simple CNN and understand the concepts of convolution and pooling. [code] [paper]

Section 3: Implementing Papers: A short view back to the past -- 2 weeks

Building a simple RNN -- Learn the basics of Recurrent Neural Networks, understand the concepts of LSTM and GRU cells. [code] [reference]

Building a GAN -- Learn how to build a simple Generative Adversarial Network, understand the concept of adversarial training. [code] [paper]

Section 4: Advanced Deep Learning: A “high” level deep learning -- 4 weeks

Building a Transformer -- Learn about the transformer architecture and its application in NLP tasks. [paper]

Building a Stable Diffusion model -- Learn about the Stable Diffusion architecture and its application in image generation tasks. [paper]