Data Science Weekly Newsletter

Issue

167

February 2, 2017

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Editor's Picks

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Unlearning descriptive statistics
If you've ever used an arithmetic mean, a Pearson correlation or a standard deviation to describe a dataset, I'm writing this for you. Better numbers exist to summarize location, association and spread: numbers that are easier to interpret and that don't act up with wonky data and outliers...

Rapping-neural-network
I made this for my high school's programming club. It's a neural network that has been trained on rap songs, and can rearrange any lyrics you feed it into a song that rhymes and has a flow...

Deep Learning Dead Languages
It is a tingling sense of presence in the room, when I finally press play on the generated audio file, and hear my trained deep-learning neural net try to formulate new and never before spoken sentences in a language where the last fluent speaker passed away in 2003...

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Get hired as a data scientist with 1-on-1 mentorship from an expert

In data science, one size does not fit all. That's why Thinkful's 1-on-1 mentorship is so critical. Thinkful's Flexible Data Science Bootcamp
teaches you Python, data analysis, and machine learning methods through real-world projects. Start learning 1-on-1 with an experienced data scientist, without quitting your day job.

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Training a deep learning model to steer a car in 99 lines of code
Today, we’ll explore one such application of deep learning. We’ll use the Udacity self-driving car nanodegree program simulator to train a generalized steering model in under 100 lines of code...

Astronomers explore uses for AI-generated images
In recent years, neural networks have made huge strides in learning to recognize and interpret information in pictures, videos and speech. But now, computer scientists such as Clune are turning those artificial-intelligence (AI) systems on their heads to create ‘generative’ networks that churn out their own realistic-seeming information...

Google Play Store: Machine Learning to Fight Spam and Abuse at Scale
In Part 1 and Part 2 of this series on app discovery, we discussed using machine learning to gain a deeper understanding of the topics associated with an app, and a deep learning framework to provide personalized recommendations. In this post, we discuss a machine learning approach to fight spam and abuse on apps section of the Google Play Store, making it a safe and trusted app platform for more than a billion Android users...

NeuroEvolution on Autonomous Car Pathing
The development of autonomous vehicles is an exciting technology that has really taken off in recent years. As a F1 fan and software developer I was especially inspired by this technology and wanted to see if I could introduce some principles of autosports into a basic self-driving model. The goal of this project was to create an autonomous driving model capable of finding the optimal racing line using neural networks optimized through genetic algorithms...

Data Science and DevOps: A Success Story
Because of the role as a decision making system, data science has to be in the core of the business processes. This fact brings a whole bunch of serious problems and some of them, especially those of cultural nature can be catastrophic. Half-hearted attempts lead to a waste of time and money – at best – and nourish data science’s reputation as a troublemaker. Properly integrated data science, however, is a game changer you cannot afford to ignore. Embrace data science with a DevOps mindset...

CommAI: Evaluating the first steps towards a useful general AI
With machine learning successfully applied to new daunting problems almost every day, general AI starts looking like an attainable goal. However, most current research focuses instead on important but narrow applications, such as image classification or machine translation. We believe this to be largely due to the lack of objective ways to measure progress towards broad machine intelligence. In order to fill this gap, we propose here a set of concrete desiderata for general AI, together with a platform to test machines on how well they satisfy such desiderata...

Learning Policies For Learning Policies — Meta Reinforcement Learning (RL²) in Tensorflow
Reinforcement Learning provides a framework for training agents to solve problems in the world. One of the limitations of these agents however is their inflexibility once trained. They are able to learn a policy to solve a specific problem (formalized as an MDP), but that learned policy is often useless in new problems, even relatively similar ones...

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Data Scientist (M/F) - Deutsche Post DHL Group - Bonn, Germany
Do you want to contribute to making the leading global logistics company more data-driven? If so, join our growing central Data Analytics Team of Deutsche Post DHL Group. We work with our operative departments to solve concrete business problems by using mathematical methods, such as time series analysis, Operations Research methods and machine learning. You will build analytical models to answer a wide variety of questions. For example, you will predict daily shipment volumes for accurate operational capacity planning, and you will handle the real-time optimization of costs and utilization of our global transport network, which includes more than 200 countries.
You should have a deep knowledge of statistics and/or mathematical optimization, be skilled in scripting languages (Python, R) and be experienced in SQL. We are looking for energetic and success-oriented problem solvers who are comfortable explaining complex analytical and technical content to various audiences....

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41 Essential Machine Learning Interview Questions (with answers)
Here is a curated and created a list of key questions that you could see in a machine learning interview...

Million requests per second with Python
Japronto is a brand new micro-framework tailored for your micro-services needs. It’s main goals include being fast, scalable and lightweight. It lets you do synchronous and asynchronous programming with asyncio and it’s shamelessly fast. Even faster than NodeJS and Go...

Adversarial Variational Bayes toy example
This notebook implements the toy example from: Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks...

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Bayes Theorem: A Visual Introduction For Beginners
"This book takes what can be a daunting and complex subject and breaks it down with a series of easy to follow examples which buildup to deliver a great overall explanation of how to use Bayes Theorem for basic analysis and even off-the-cuff critical thinking"...

For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page...

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