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Diagnosing Heart Diseases with Deep Neural Networks

Diagnosing Heart Dis...

The Second National Data Science Bowl, a data science competition where the goal was to automatically determine cardiac volumes from MRI scans, has just ended. We participated with a team of 4 members from the Data Science lab at Ghent University in Belgium and finished 2nd! The team kunsthart (artificial heart in English) consisted of […]

Getting Started with Deep Learning

Getting Started with...

This was originally posted on the Silicon Valley Data Science blog. At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the model performance. However, when we researched what technologies were […]

Deep Learning: the Silver bullet?

Deep Learning: the S...

In 2016, we saw a wide range of breakthroughs having to do with artificial intelligence and deep learning in particular. Google, Facebook, and Baidu announced several breakthroughs using deep learning. Google also defeated Go. Deep learning is one specific class of machine learning algorithms. It has a long history, taking its roots in the earlier […]

Recommending music on Spotify with Deep Learning

Recommending music o...

Overview This is going to be a long post, so here’s an overview of the different sections. If you want to skip ahead, just click the section title to go there. Collaborative filtering A very brief introduction, its virtues and its flaws. Content-based recommendation What to do when no usage data is available. Predicting listening […]

How to train your… Deep Neural Network

How to train your...

There are certain practices in Deep Learning that are highly recommended, in order to efficiently train Deep Neural Networks. In this post, I will be covering a few of these most commonly used practices, ranging from importance of quality training data, choice of hyperparameters to more general tips for faster prototyping of DNNs. Most of […]

10 Basic Deep Learning Questions

10 Basic Deep Learni...

Deep Learning in not Good Old Fashion AI (GOFAI), Expert systems, semantic web, deductive logic systems etc. are examples of systems that are based on symbolic logic. These systems are typically associated with AI. These do work, however they have one short coming, they are unable to effectively learn from the data. Originally posted at […]

Representation Learning Bonus Material

Representation Learn...

This post is part of a three part series. Notes on Representation Learning Notes on Representation Learning Continued Representation Learning Bonus Material Using GANs to Generate Images Based On Text Descriptions Below are some neat pictures demonstrating the use of GANs to generate images based on text descriptions.  All the images below are generated by a […]

Notes on Representation Learning Continued

Notes on Representat...

This post is part of a three part series. Notes on Representation Learning Notes on Representation Learning Continued Representation Learning Bonus Material Ten Shot Learning with Generative Adversarial Networks A very exciting approach to representation learning (but one that sadly does not work on discrete values like text, at least not without some modification) are Generative […]

Notes on Representation Learning

Notes on Representat...

TL;DR: Representation learning can eliminate the need for large labeled data sets to train deep neural networks, opening up new domains to machine learning and transforming the practice of Data Science. Check out “Notes on Representation Learning” in these three parts. Notes on Representation Learning Notes on Representation Learning Continued Representation Learning Bonus Material Deep Learning and […]