Tom Joy

Tom Joy

Entrepreneur (AI), Co-founder of GirlsWhoML, Prev TVG (PhD), Meta (intern), Five AI (Researcher)

Blog posts

2025

Generative Models in AI, GANs, VAEs, INNs

13 minute read

Published:

Generative Models in AI, GANs, VAEs, INNs

The main goal of generative model is the ‘generate’ some new data, unlike in classification where we have a fixed set of classes we want to predict. It’s basically producing rather than predicting. There are four main types of generative models: VAEs, GANs, Normalizing Flows, Invertible Neural Networks and Diffusion Models (for next time), and they all try and learn a model which captures the underlying structure of the data.

Intro to Deep Learning 2 – Loss Landscapes, Trainig Dynamics, and Optimizers

9 minute read

Published:

Loss Landscapes

In the last post we spoke about walking down the hill to find the location of the lowest point. This is a great analogy for training a neural network, we want to find the parameters that minimize the loss function. You can think of the loss function as the height of the hill, and the parameters as the coordinates of the hill. This landscape is called the loss landscape, and we talk alot about it in the deep learning community. Specifically in relation to valleys, local minima, saddle points, and flat regions. Whenever you hear these terms, just think about hills and valleys.

Intro to Deep Learning – Training, Neural Networks, and Feature Representations

9 minute read

Published:

Intro

Basically every machine learning AI problem is now solved using deep learning. Deep learning is the approach of using neural networks to learn from data, in this approach we don’t need to decide on what the model (neural network) should do, we just need to give it the data and the model will learn to do the task.