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Data Science Weekly Newsletter
September 15, 2016
Why does deep and cheap learning work so well?
We show how the success of deep learning depends not only on mathematics but also on physics: although well-known mathematical theorems guarantee that neural networks can approximate arbitrary functions well, the class of functions of practical interest can be approximated through "cheap learning" with exponentially fewer parameters than generic ones, because they have simplifying properties tracing back to the laws of physics...
Human in AI Loop
A.I is out to get us. It will replace us all.. or maybe, it’s just another tool that we will get used to like calculators or iPhones...
Machine Learning in a Year
From being a total ml noob to start using it at work. This is a follow up to an article I wrote last year, Machine Learning in a Week, on how I kickstarted my way into machine learning (ml) by devoting five days to the subject...
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Data Science Articles & Videos
Deep Learning Isn't a Dangerous Magic Genie. It's Just Math
Deep Learning rapidly ‘eating’ artificial intelligence. But let’s not mistake this ascendant form of artificial intelligence for anything more than it really is. The famous author Arthur C. Clarke wrote, “Any sufficiently advanced technology is indistinguishable from magic.” And deep learning is certainly an advanced technology—it can identify objects and faces in photos, recognize spoken words, translate from one language to another, and even beat the top humans at the ancient game of Go. But it’s far from magic...
Generative Visual Manipulation on the Natural Image Manifold
Realistic image manipulation is challenging because it requires modifying the image appearance in a user-controlled way, while preserving the realism of the result. Unless the user has considerable artistic skill, it is easy to "fall off" the manifold of natural images while editing. In this paper, we propose to learn the natural image manifold directly from data using a generative adversarial neural network...
Plenary Panel: Is Deep Learning the New 42?
According to Douglas Adams’s famous “Hitchhiker’s Guide to the Galaxy” after 7.5 millions years of work the “Deep Thought” computer categorically found out that 42 is the “Answer to the Ultimate Question of Life, the Universe, and Everything” (although unfortunately, no one knows exactly what that question was). Rather than wait another 7.5 million years for “Deep Thought” to answer our quest we have assembled a distinguished panel of experts to give us their opinion on deep learning and its present and future impact....
Running a Deep Learning (Dream) Machine
I’m used to working in the cloud and will keep doing so for production-oriented systems/algorithms. There are however huge drawbacks to cloud-based systems for more research oriented tasks where you mainly want to try out various algorithms and architectures, to iterate and move fast. To make this possible I decided to custom design and build my own system specifically tailored for Deep Learning, stacked full with GPUs. This turned out both more easy and more difficult than I imagined. In what follows I will share my “adventure” with you. I hope it will be useful for both novel and established Deep Learning practitioners...
Head of FCO Data Science - Foreign & Commonwealth Office - London
The Foreign & Commonwealth Office (FCO) promotes the United Kingdom's interests overseas, supporting our citizens and businesses around the globe.
As Head of FCO Data Science, it’ll be down to you to establish the FCO’s new data science capability. Leading the team, you’ll:
Advise senior stakeholders on the sound use of data
Provide insight to improve policy making
Evaluate the impact of Her Majesty’s Government (HMG) interventions
Help predict future instability, threats and opportunity for prosperity
This is a role at the cutting edge of foreign policy and a truly unique opportunity to make a real impact on the world. To join us, you’ll need:
An analytical Master’s degree
Substantial data science experience – including practice in Python or R
An understanding of Regression Analysis, Natural Language Processing and Sentiment Analysis
The Neural Network Zoo
With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containing many of those architectures....
Simplest example of Machine Learning in TensorFlow...
Scikit-Optimize, or skopt, is a simple and efficient library for sequential model-based optimization, accessible to everybody and reusable in various contexts. The library is built on top of NumPy, SciPy and Scikit-Learn...
Test-Driven Machine Learning
The book begins with an introduction to test-driven machine learning and quantifying model quality. From there, you will test a neural network, predict values with regression, and build upon regression techniques with logistic regression...
For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page...