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.
Hi there, and welcome to my blog.
Here you'll find resources about data science, software engineering, and life. Well, maybe not life...
Hope you enjoy.
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.
TensorFlow — The Scope of Software Engineering
How to structure your TensorFlow graph like a software engineer.
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.
How to Build Your Personal Brand as a Data Scientist
A couple of months ago I embarked on a journey to build my personal brand as a data scientist, and I want to share how I did it with you.
The Story of a Bad Train-Test Split
Splitting your dataset to train-test sets can sometimes be more complicated than one might expect.
TensorFlow Filesystem - Access Tensors Differently
The weird (but cool) way to access a tensorflow model by mounting it into a filesystem.
Variational Autoencoders Explained in Detail
Learn all the details needed to implement a variational autoencoder, code included.