Data Science Weekly Newsletter

Issue

147

September 15, 2016

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Editor's Picks

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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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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...

Customer Service Bots Are Getting Better at Detecting Your Agitation
A virtual assistant that can tell you’re frustrated can help you out...

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...

Self-Driving Cars Can Learn a Lot by Playing Grand Theft Auto
Hyper-realistic computer games may offer an efficient way to teach AI algorithms about the real world...

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....

Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks
We consider scenarios from the real-time strategy game StarCraft as new benchmarks for reinforcement learning algorithms. We propose micromanagement tasks, which present the problem of the short-term, low-level control of army members during a battle...

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...

Measles Incidence in Altair
This is an example of reproducing the Wall Street Journal's famous Measles Incidence Plot in Python using Altair...

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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....

Scikit-Optimize
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...

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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...

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