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NLP and Sarcasm: What’s the Deal?

Sarcasm is incredibly hard for chatbots and NLP applications. Here, we’ll take a look at why. Sarcasm or Not? As humans, you and I can look at these two chats and determine that in the first the person appears to be sincere while in the other comes off sarcastic and cold simply

Do you know what I mean?

Why thinking too much is just as bad as thinking too little. The cognitive trap Imagine you’re standing in front of your computer, much like you are now, and you’re prompted to play a game. “You are playing this game with thousands of people all over the world”, you’re told. Intrigued as to

Teaching an AI to Write Pop Music

Because who has time to write it themselves anymore? I was never any good at writing lyrics. In my high-school ska band, I wrote the horn parts and some other instrumentals and in my college a capella group, I was always just arranging covers of songs that that had already

How to Build A Data Set For Your Machine Learning Project

Are you about thinking AI for your organization? You have identified a use case with a proven ROI? Perfect! but not so fast… do you have a data set? Well, most companies are struggling to build an AI-ready data set or perhaps simply ignore this issue, I thought that this article

Introduction to gradient boosting on decision trees with Catboost

Today I would like to share my experience with open source machine learning library, based on gradient boosting on decision trees, developed by Russian search engine company — Yandex. Github profile according to the 12th of February Library is released under Apache license and offered as a free service. ‘Cat’, by the way,

Deeper into DCGANs

My last post about DCGANs was primarily focused on the idea of replacing fully connected layers with convolutions and implementing upsampling convolutions with Keras. This article will further explain the architectural guidelines mentioned by Raford et al. [1], as well as additional topics mentioned in the paper such as Unsupervised

Reinforcement Learning Tutorial Part 2: Cloud Q-learning

In part 1, we looked at the theory behind Q-learning using a very simple dungeon game with two strategies: the accountant and the gambler. This second part takes these examples, turns them into Python code and trains them in the cloud, using the Valohai deep learning management platform. Due to

FastAI Image Classification

Creating model and initial training The FastAI library is designed to let you create models (FastAi calls them learners) with only a few lines of code. They provide a method called create_cnn, which can be used to create a convolutional neural network. The method needs two arguments, the data and the

Finding Your Flavor of Data Science Career

Three Approaches to Guide You in Choosing Your Path Does your concept of a Data Scientist look something like a fictional super hero, possessing such a broad and deep skillset that it is simply humanly impossible? And yet, does that unrealistic image make you sometimes feel like a data science imposter?

Building fully custom machine learning models on AWS SageMaker: a practical guide

AWS SageMaker is a cloud machine learning SDK designed for speed of iteration, and it’s one of the fastest-growing toys in the Amazon AWS ecosystem. Since launching in late 2017 SageMaker’s growth has been remarkable — last year’s AWS re:Invent stated that there are now over 10,000 companies using SageMaker to standardize