Another Datum

A blog by Yoel Zeldes

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GPT-3, a Giant Step for Deep Learning and NLP

A few days ago, OpenAI announced a new successor to their Language Model (LM) - GPT-3. This is the largest model trained so far, with 175 billion parameters. While training this large model has its merits, reading a large portion of 72 pages can be tiresome. In this blog post I’ll highlight the parts that I find interesting for people familiar with...

The accessibility of GPT-2 - text generation and fine-tuning

Natural Language Generation (NLG) is a well studied subject among the NLP community. With the rise of deep learning methods, NLG has become better and better. Recently, OpenAI has pushed the limits, with the release of GPT-2 - a Transformers based model that predicts the next token at each time space. Nowadays it’s quite easy to use these models -...

Mixture of Variational Autoencoders - a Fusion Between MoE and VAE

The Variational Autoencoder (VAE) is a paragon for neural networks that try to learn the shape of the input space. Once trained, the model can be used to generate new samples from the input space. If we have labels for our input data, it’s also possible to condition the generation process on the label. In the MNIST case, it means we can specify which...

TensorFlow — The Scope of Software Engineering

So you’ve finished training your model, and it’s time to get some insights as to what it has learned. You decide which tensor should be interesting, and go look for it in your code — to find out what its name is. Then it hits you — you forgot to give it a name. You also forgot to wrap the logical code block with a named scope. It means you’ll have...

Preparing for the Unexpected

Some of the problems we tackle using machine learning involve categorical features that represent real world objects, such as words, items and categories. So what happens when at inference time we get new object values that have never been seen before? How can we prepare ourselves in advance so we can still make sense out of the input? Unseen values,...

Think your Data Different

In the last couple of years deep learning (DL) has become a main enabler for applications in many domains such as vision, NLP, audio, click stream data etc. Recently researchers started to successfully apply deep learning methods to graph datasets in domains like social networks, recommender systems and biology, where data is inherently structured...

How to Build Your Personal Brand as a Data Scientist

Personal branding is a thing now. It always has been, but I believe it’s been getting more and more attention recently. More people are aware of its importance, including the employers. Giving you a big paycheck, assuming you’re good, is obvious. Providing opportunities to flourish and build your personal brand is something an increasing number...

The Story of a Bad Train-Test Split

About a year ago we incorporated a new type of feature into one of our models used for recommending content items to our users. I’m talking about the thumbnail of the content item: Up until that point we used the item’s title and metadata features. The title is easier to work with compared to the thumbnail — machine learning wise. Our model has...

TensorFlow Filesystem - Access Tensors Differently

Tensorflow is great. Really, I mean it. The problem is it’s great up to a point. Sometimes you want to do very simple things, but tensorflow is giving you a hard time. The motivation I had behind writing TFFS (TensorFlow File System) can be shared by anyone who has used tensorflow, including you. All I wanted was to know what the name of a specific...

Variational Autoencoders Explained in Detail

In the previous post of this series I introduced the Variational Autoencoder (VAE) framework, and explained the theory behind it. In this post I'll explain the VAE in more detail, or in other words - I'll provide some code :) After reading this post, you'll understand the technical details needed to implement VAE. As a bonus point, I'll show you...

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