Data Science Weekly - Issue 135
Issue #135 June 23 2016
Editor Picks
The Business Implications of Machine Learning
We’ve finely honed this defense mechanism, for good purpose. It’s better to focus on what’s in front of us than the flavor of the week. CRISPR might change our lives, but knowing how it works doesn’t help you. VR could eat all media, but it’s hardware requirements keep it many years away from common use. But please: do not ignore machine learning...
How Google Is Remaking Itself for “Machine Learning First”
If you want to build artificial intelligence into every product, you better retrain your army of coders. Check...
One year as a Data Scientist at Stack Overflow
Last Thursday (June 16th) marks my one-year anniversary of working at Stack Overflow as a Data Scientist. I’d finished my PhD about a month before I joined, and my move to a tech company was a pretty big change for me. As of only a few months earlier, I’d been planning to stay in academic research, particularly in the field of computational biology. I’d started applying for postdoctoral fellowships, and hadn’t even considered applying to “industry” jobs. What changed my mind?...
A Message from this week's Sponsor:
Data Science in Practice: Five Common Applications of Data Science with Concrete, Real-Life Use Cases via @YhatHQ
In this whitepaper we debunk the impression that data science is some type of obscure black magic and give you concrete examples of how it's applied in reality. You'll learn how real companies are using data science to make their products and day-to-day operations better.
Data Science Articles & Videos
Google Gets Practical about the Dangers of AI
The company lays out five unsolved challenges that need to be addressed if smart machines such as domestic robots are to be safe...
Peeking inside Convnets
Convolutional neural networks are used extensively for a number of image related tasks these days. Despite being very successful, they're mostly seen as "black box" models, since it's hard to understand what happens inside the network. There are however methods to "peek inside" the convnets, and thus understand a bit more about how they work...
Music Transcription with Convolutional Neural Networks
I'm going to try to make the case that note detection in music is essentially image recognition with a few small differences and I'll describe some techniques I used to modify neural networks from computer vision to produce sheet music transcriptions of (polyphonic) music that are actually quite playable...
In deep learning, architecture engineering is the new feature engineering
Two of the most important aspects of machine learning models are feature extraction and feature engineering. Those features are what supply relevant information to the machine learning models...
Machine-vision algorithms help craft realistic portraits from sketches
Scientists taught a neural network to complete this heady task with ease...
Germany most likely to win Euro 2016
Based on publicly available data and the gamlls R-package they built a model to forecast probabilities of win, tie and loss for any game of Euro 2016 (Actually, they even get probabilities on a more precise level with an exact number of goals for both teams. For more details on the model take a look at their preliminary technical report).This is what their model deems as most likely tournament evolution this time...
MITs New AI Can (Sort of) Fool Human's with Sound Effects
Neural networks are already beating us at games, organizing our smartphone photos, and answering our emails. Eventually, they could be filling jobs in Hollywood...
Visualizing Bayesian Updating
One of the most straightforward examples of how we use Bayes to update our beliefs as we acquire more information can be seen with a simple Bernoulli process. That is, a process which has only two possible outcomes...I’ve put together this little piece of R code to help visualize how our beliefs about the probability of success (heads, functioning widget, etc) are updated as we observe more and more outcomes...
Jobs
Data Scientist - Dish Network - Colorado We have a bold sense of pride, adventure, and desire to win – it’s in our DNA. And we’re looking for people with boundless energy, intelligence, and an overwhelming need to achieve to join our team as we embark on the next chapter of our story...
Training & Resources
Implementing your own recommender systems in Python
In this tutorial, you will implement Model-Based CF by using singular value decomposition (SVD) and Memory-Based CF by computing cosine similarity. We will use MovieLens dataset, which is one of the most common datasets used when implementing and testing recommender engines...
Using Anomaly Detectors to Assess Covariate Shift
BigML provides two anomaly detector functions in WhizzML useful for building a data shift detector...
Coconut
Coconut is a simple, elegant, Pythonic functional programming language that compiles to Python...
Books
Data Scientists At Work A collection of interviews with sixteen of the world's most influential and innovative data scientists...
For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
P.S. Interested in reaching fellow readers of this newsletter? Consider sponsoring! Email us for details :) - All the best, Hannah & Sebastian