Data Science Weekly - Issue 214
Issue #214 Dec 28 2017
Editor Picks
A non-comprehensive list of awesome things other people did in 2017
Editor’s note: For the last few years I have made a list of awesome things that other people did (2016,2015, 2014, 2013). Like in previous years I’m making a list, again right off the top of my head. If you know of some, you should make your own list or add it to the comments!...
Deep Learning Hardware Limbo
With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. So for consumers, I cannot recommend buying any hardware right now. The most prudent choice is to wait until the hardware limbo passes. This might take as little as 3 months or as long as 9 months. So why did we enter deep learning hardware limbo just now?...
Deep Learning Achievements Over the Past Year:
Great developments in text, voice, and computer vision technologies
At Statsbot, we’re constantly reviewing the deep learning achievements to improve our models and product. Around Christmas time, our team decided to take stock of the recent achievements in deep learning over the past year (and a bit longer). We translated the article by a data scientist, Ed Tyantov, to tell you about the most significant developments that can affect our future...
A Message from this week's Sponsor:
A cool developer survey with an exclusive prize for a DataScienceWeekly Participant
What do you use Data Science for? What is your most important goal in Data Science? Complete the developer economics survey to share your views, learn about new tools, discover your cyberpunk developer character or chance to win $70 USD credit for software or Amazon, exclusive to DSW participants. More nifty prizes await... Hurry, the survey is live until Dec. 31st! Enter Here!
Data Science Articles & Videos
These Stunning A.I. Tools Are About to Change the Art World
What happens to artists when machines get more creative control?...
Why your relationship is likely to last, or not
Local Interpretable Model-Agnostic Explanations. A talk by Friederike Schuur at PyData NYC 2017...
2017: The Year in Color
Looking back on 2017, what did our world look like in color? We analyzed over 30 million English-language news documents to find out...
Fair and Balanced? Thoughts on Bias in Probabilistic Modeling
In recent months and years, the Machine Learning community has conducting a notable amount of soul searching on the question of algorithmic bias: are our algorithms operating in ways that are fundamentally unfair towards specific groups within society?...
Episode #144: Machine Learning at the Large Hadron Collider
We all know Python is becoming increasingly important in both science and machine learning. This week we journey to the very forefront of Physics. You will meet Michela Paganini, Michael Kagan, and Matthew Feickert. They all work at the Large Hadron Collider and are using Python and machine learning to help make the next major discovery in Physics...
Four deep learning trends from ACL 2017:
Part One: Linguistic Structure and Word Embeddings
In this two-part post, I describe four broad research trends that I observed at the conference (and its co-located events) through papers, presentations and discussions. The content is guided entirely by my own research interests; accordingly it’s mostly focused on deep learning, sequence-to-sequence models, and adjacent topics...
The Case for B-Tree Index Structures
Now I am all in favor of trying out new ideas, and adapting to the data distribution is clearly a good idea, but do we really need a neural network for that? Because, after all, the neuronal network is just an approximation of the CDF function. There are many other ways to approximate a function, for example spline interpolation: We define a few knots of the spline, and then interpolate between the knots...
Baby steps with CNTK and F#
So what have I been up to lately? Obsessing over CNTK, the Microsoft deep-learning library. Specifically, the team released a .NET API, which got me interested in exploring how usable this would be from the F# scripting environment. I started a repository to try out some ideas already, but, before diving into that in later posts, I figure I could start by a simple introduction, to set some context...
Jobs
Data Science & Analytics Associate - PepsiCo eCommerce - NYC eCommerce is one of the fastest-growing areas within the consumer products industry and represents a significant opportunity to accelerate growth for PepsiCo going forward.
To ensure we win in this space we have established a dedicated eCommerce group, bringing together world class talent across F&B, digital, and key customers. While tied closely to broader PepsiCo, the eCommerce group has a unique start-up feel and defined values that embrace a more entrepreneurial mindset: bias for action; results oriented; community-focused; prioritization of people.
In order to maintain the necessary pace to meet the growth targets and compete effectively against start-ups or technology competitors requires a step-change in our thinking and traditional approaches to data analytics and utilization. Accordingly, we are seeking a Data Scientist to manage data entry and integrity, data clean-up, query-based analysis, and project management with business users...
Training & Resources
Get A TensorFlow Tensor By Name
Learn how to get A TensorFlow Tensor by name by using the TensorFlow get_default_graph operation and then the TensorFlow get_tensor_by_name operation... Video screencast and associated full written transcript tutorial...
Tutorial on Deep Generative Models
At the end of this tutorial, audience member will have a full understanding of the latest advances in generative modelling covering three of the active types of models: Markov models, latent variable models and implicit models, and how these models can be scaled to high-dimensional data...
Interpretable Machine Learning
A Guide for Making Black Box Models Explainable...
Books
Concrete Mathematics: A Foundation for Computer Science An insightful, revealing history of how mathematics transformed our world...
"This is fun stuff. It's an interesting take on discrete math. In fact, it's really not discrete math; in includes discrete math but also includes other elements. I think this is especially good for the CS people, which is actually the intended audience..."
For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
P.S., Want to reach our audience / fellow readers? Consider sponsoring - grab a spot now; first come first served! All the best, Hannah & Sebastian