GPT-3, a Giant Step for Deep Learning and NLP
Can intelligence emerge simply by training a big enough language model using lots of data? OpenAI tries to do so, using 175 billion parameters.
The Accessibility of GPT-2 - Text Generation and Fine-tuning
Text generation using GPT-2 is quite easy, using the right tools. Learn how to do it, as well as how to fine-tune the model on your own dataset.
Mixture of Variational Autoencoders - a Fusion Between MoE and VAE
An unsupervised approach to digit classification and generation.
Preparing for the Unexpected
How to apply your model to input it has never seen before.
Think your Data Different
Learn how node2vec works, and what kind of information it captures that word2vec doesn’t — includes case study.
Variational Autoencoders Explained in Detail
Learn all the details needed to implement a variational autoencoder, code included.
How to Engineer Your Way Out of Slow Models
So you just finished designing that great neural network architecture. But how do you handle the fact it is slow?
Zooming Past the Competition
How to create an Augmented Reality app that allows a user to get content recommendations.
Variational Autoencoders Explained
Ever wondered how the Variational Autoencoder model works? Keep reading to find out.
Uncertainty for CTR Prediction: One Model to Clarify Them All
Learn how to handle uncertainty in recommender systems in a principled way using one unified model.