A Gentle Visual Intro to Data Analysis in Python Using Pandas

If you’re planning to learn data analysis, machine learning, or data science tools in python, you’re most likely going to be using the wonderful pandas library. Pandas is an open source library for data manipulation and analysis in python.

Loading Data

One of the easiest ways to think about that, is that you can load tables (and excel files) and then slice and dice them in multiple ways:

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The Illustrated Transformer

In the previous post, we looked at Attention – a ubiquitous method in modern deep learning models. Attention is a concept that helped improve the performance of neural machine translation applications. In this post, we will look at The Transformer – a model that uses attention to boost the speed with which these models can be trained. The Transformers outperforms the Google Neural Machine Translation model in specific tasks. The biggest benefit, however, comes from how The Transformer lends itself to parallelization. It is in fact Google Cloud’s recommendation to use The Transformer as a reference model to use their Cloud TPU offering. So let’s try to break the model apart and look at how it functions.

The Transformer was proposed in the paper Attention is All You Need. A TensorFlow implementation of it is available as a part of the Tensor2Tensor package. Harvard’s NLP group created a guide annotating the paper with PyTorch implementation. In this post, we will attempt to oversimplify things a bit and introduce the concepts one by one to hopefully make it easier to understand to people without in-depth knowledge of the subject matter.

A High-Level Look

Let’s begin by looking at the model as a single black box. In a machine translation application, it would take a sentence in one language, and output its translation in another.

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Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)

May 25th update: New graphics (RNN animation, word embedding graph), color coding, elaborated on the final attention example.

Note: The animations below are videos. Touch or hover on them (if you’re using a mouse) to get play controls so you can pause if needed.

Sequence-to-sequence models are deep learning models that have achieved a lot of success in tasks like machine translation, text summarization, and image captioning. Google Translate started using such a model in production in late 2016. These models are explained in the two pioneering papers (Sutskever et al., 2014, Cho et al., 2014).

I found, however, that understanding the model well enough to implement it requires unraveling a series of concepts that build on top of each other. I thought that a bunch of these ideas would be more accessible if expressed visually. That’s what I aim to do in this post. You’ll need some previous understanding of deep learning to get through this post. I hope it can be a useful companion to reading the papers mentioned above (and the attention papers linked later in the post).

A sequence-to-sequence model is a model that takes a sequence of items (words, letters, features of an images…etc) and outputs another sequence of items. A trained model would work like this:

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Visualizing Pandas' Pivoting and Reshaping Functions

I love using python’s Pandas package for data analysis. The 10 Minutes to pandas is a great place to start learning how to use it for data analysis.

Things get a lot more interesting once you’re comfortable with the fundamentals and start with Reshaping and Pivot Tables. That guide shows some of the more interesting functions of reshaping data. Below are some visualizations to go along with the Pandas reshaping guide.

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A Visual and Interactive Guide to the Basics of Neural Networks

Update: Part 2 is now live: A Visual And Interactive Look at Basic Neural Network Math

Motivation

I’m not a machine learning expert. I’m a software engineer by training and I’ve had little interaction with AI. I had always wanted to delve deeper into machine learning, but never really found my “in”. That’s why when Google open sourced TensorFlow in November 2015, I got super excited and knew it was time to jump in and start the learning journey. Not to sound dramatic, but to me, it actually felt kind of like Prometheus handing down fire to mankind from the Mount Olympus of machine learning. In the back of my head was the idea that the entire field of Big Data and technologies like Hadoop were vastly accelerated when Google researchers released their Map Reduce paper. This time it’s not a paper – it’s the actual software they use internally after years and years of evolution.

So I started learning what I can about the basics of the topic, and saw the need for gentler resources for people with no experience in the field. This is my attempt at that.

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Supercharging Android Apps With TensorFlow (Google's Open Source Machine Learning Library)

google-tensorflow-android.jpg

In November 2015, Google announced and open sourced TensorFlow, its latest and greatest machine learning library. This is a big deal for three reasons:

  1. Machine Learning expertise: Google is a dominant force in machine learning. Its prominence in search owes a lot to the strides it achieved in machine learning.
  2. Scalability: the announcement noted that TensorFlow was initially designed for internal use and that it’s already in production for some live product features.
  3. Ability to run on Mobile.

This last reason is the operating reason for this post since we’ll be focusing on Android. If you examine the tensorflow repo on GitHub, you’ll find a little tensorflow/examples/android directory. I’ll try to shed some light on the Android TensorFlow example and some of the things going on under the hood.

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