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Data Science Weekly – Issue 178

By | machinelearning, TensorFlow

Data Science Weekly – Issue 178

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Curated news, articles and jobs related to Data Science. 
Keep up with all the latest developments

Issue #178

April 20 2017

Quick Question For You: Do you want a Data Science job?
"No": Scroll on down to the regular newsletter!

"Yes": Great news! After helping hundred of readers like you get Data Science jobs, we've distilled all the real-world-tested advice into a self-directed course that guides you in constructing your own highly personalized plan for what you need to learn and what you can safely ignore – saving you time, effort, and worry.

The course is broken down into three guides:

  1. Data Science Getting Started Guide. This guide shows you how to figure out the knowledge gaps that MUST be closed in order for you to become a data scientist quickly and effectively (as well as the ones you can ignore)
  2. Data Science Project Portfolio Guide. This guide teaches you how to start, structure, and develop your data science portfolio with the right goals and direction so that you are a hiring manager's dream candidate
  3. Data Science Resume Guide. This guide shows how to make your resume promote your best parts, what to leave out, how to tailor it to each job you want, as well as how to make your cover letter so good it can't be ignored!

For more details about each specific guide, and to see if it can help you as much as it's helped others, click here to learn more.

Cheers,
Hannah & Sebastian.

Now on to this week's newsletter!…

———————————————————–

Editor Picks

 

 


 

A Message from this week's Sponsor:

 

 

  • [WHITEPAPER] Applied Data Science by Yhat, Inc.

    This is a white paper about data science teams and how companies apply their insights to the real world. You’ll learn how successful data science teams are composed and operate and which tools and technologies they are using.
     


 

Data Science Articles & Videos

 

  • Facebook’s Perfect, Impossible Chatbot
    Facebook is quietly trying to develop the most useful virtual assistant ever, in a project that illustrates the current limitations of artificial intelligence…
  • A Neural Parametric Singing Synthesizer
    We present a new model for singing synthesis based on a modified version of the WaveNet architecture. Instead of modeling raw waveform, we model features produced by a parametric vocoder that separates the influence of pitch and timbre. This allows conveniently modifying pitch to match any target melody, facilitates training on more modest dataset sizes, and significantly reduces training and generation times…
  • NBA Foul Calls and Bayesian Item Response Theory
    Since 2015, the NBA has released a report reviewing every call and non-call in the final two minutes of every NBA game where the teams were separated by five points or less with two minutes remaining. The NBA is certainly marketed as a star-centric league, so this data set presents a fantastic opportunity to understand the extent to which the players involved in a decision impact whether or not a foul is called…
  • Predicting Churn without Machine Learning
    In this post I will describe a way of predicting churn based on customers' inactivity profile that I've applied in various client engagements. Without using machine learning algorithms, the model delivers an interpretable prediction of churn that gives a fairly accurate insight into the customers leaving the base…
  • Gender Roles with Text Mining and N-grams
    Today is the one year anniversary of the janeaustenr package’s appearance on CRAN, its cranniversary, if you will. I think it’s time for more Jane Austen here on my blog….
  • Stitchfix- The Making of the Tour, Part 2: Simulations
    In our first installment of this Making of the Tour series we gave a general overview of our development process and our scrollytelling code structure. Now we get to dig into some details. In this post, we’ll talk about some simulation-powered animations, provide some cleaned-up code that you can use, and discuss these animations’ genesis and utility for visualizing abstract systems and algorithms or for visualizing real historical data and projected futures…

 


 

Jobs

 

  • Senior Data Analyst – VSCO – Oakland, CA

    VSCO is a leading creative platform with a monthly audience of over 45 million and growing.

    We are looking for a Senior Data Analyst to build data at VSCO from the ground up. You will design our data model for user behavior and content impression, and will mine the data to find insights that will influence the product roadmap. Expect to get your hands dirty with Redshift, Spark, and data visualization tools under the guidance of our Director of Data Science….

 


 

Training & Resources

 

  • Caffe2 – New release from Facebook
    Caffe2 aims to provide an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2's cross-platform libraries…

  • Numenta Anomaly Benchmark for Streaming Anomaly Detection
    With sensors invading our everyday lives, we are seeing an exponential increase in the availability of streaming, time-series data. Finding anomalies or unusual behavior in this data can be extremely valuable, but doing it reliably is quite difficult. There are dozens of anomaly detection algorithms in the literature but it is almost impossible to evaluate them for streaming because existing benchmarks focus on non-streaming batch data. We created the open source Numenta Anomaly Benchmark (NAB) to fill this hole…

 


 

Books

 

  • 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.

 


 
P.S. Looking to hire a Data Scientist? Find an awesome one among our readers! Email us for details on how to post your job 🙂 – All the best, Hannah & Sebastian

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Data Science Weekly – Issue 177

By | machinelearning, TensorFlow

Data Science Weekly – Issue 177

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Curated news, articles and jobs related to Data Science. 
Keep up with all the latest developments

Issue #177

April 13 2017

Editor Picks

 

  • A Peek at Trends in Machine Learning
    Have you looked at Google Trends? It’s pretty cool — you enter some keywords and see how Google Searches of that term vary through time. I thought — hey, I happen to have this arxiv-sanity database of 28,303 (arxiv) Machine Learning papers over the last 5 years, so why not do something similar and take a look at how Machine Learning research has evolved over the last 5 years? The results are fairly fun, so I thought I’d post…
  • Why Momentum Really Works
    Momentum can be understood far more precisely if we study it on the right model. One nice model is the convex quadratic. This model is rich enough to reproduce momentum’s local dynamics in real problems, and yet simple enough to be understood in closed form. This balance gives us powerful traction for understanding this algorithm…

 


 

A Message from this week's Sponsor:

 

 

  • Get a data science job, guaranteed.

    With personalized mentoring from industry experts, your own career coach, and exclusive employer partnerships, Springboard's new Data Science Career Track is set to guarantee you a job — or your money back.
     


 

Data Science Articles & Videos

 

  • Promoting Positive Climate Change Conversations via Twitter
    For my final project of the Metis Data Science program, I investigated the climate change conversations taking place on Twitter in March 2017. The 1 million tweets that I looked at were a snapshot in time – many users talked about EPA head Scott Pruitt’s denial that CO2 causes global warming, or the critical condition of Australia’s Great Barrier Reef. Sub-communities were also apparent within the greater conversation, including a group of climate change deniers who stood out from the rest…
  • RNN First Result – Bach Chorales
    This is the first result of my RNN trained with Bach Chorales. I'm using some vst to spice up the sound but chords and rhythm are completely generated by the net…
  • Unsupervised sentiment neuron
    We’ve developed an unsupervised system which learns an excellent representation of sentiment, despite being trained only to predict the next character in the text of Amazon reviews…
  • Federated Learning: Collaborative Machine Learning without Centralized Training Data
    Standard machine learning approaches require centralizing the training data on one machine or in a datacenter. And Google has built one of the most secure and robust cloud infrastructures for processing this data to make our services better. Now for models trained from user interaction with mobile devices, we're introducing an additional approach: Federated Learning…
  • How to fake a sophisticated knowledge of wine with Markov Chains
    To the untrained (like me), wine criticism may seem like an exercise in pretentiousness. It may seem like anybody following a set of basic rules and knowing the proper descriptors can feign sophistication (at least when it comes to wine). In this post, we will be exploiting the formulaic nature of wine reviews to automatically generate our own reviews that appear (at least to the untrained) to be legitimate…
  • CycleGAN
    Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more…

 


 

Jobs

 

  • Data Scientist – NBA – New York

    The NBA's Team Marketing and Business Operations ("TMBO") group is a unique in-house consulting arm within the NBA league office that strives to drive best practices and innovation across all 64 NBA, WNBA and NBA Development League teams. The primary focus for this fast paced and collaborative department is on all aspects of business operations, including ticket sales and service, sponsorship, marketing, digital, analytics, and data strategy.

    The Data Scientist role will be a technical expert within TMBO in all matters surrounding statistical analysis, data manipulation and interpretation, and process automation. You will be a thought leader, tasked with the responsibility to leverage the NBA's various internal data sources to create new and innovative analytical products and outputs to inform league executives about the state of team businesses…

 


 

Training & Resources

 

  • Python TensorFlow Tutorial – Build a Neural Network
    This introductory tutorial to TensorFlow will give an overview of some of the basic concepts of TensorFlow in Python. These will be a good stepping stone to building more complex deep learning networks, such as Convolution Neural Networks and Recurrent Neural Networks, in the package…

  • Explanation of Neural Turing Machines
    I haven't found a good resource for people with a technical background who are unfamiliar with the more advanced concepts and are looking for someone to fill them in. This is my attempt to bridge that gap…

 


 

Books

 

  • 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.

 


 
P.S. Looking to hire a Data Scientist? Find an awesome one among our readers! Email us for details on how to post your job 🙂 – All the best, Hannah & Sebastian

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NSynth: Neural Audio Synthesis

By | machinelearning, ML, TensorFlow

One of the goals of Magenta is to use machine learning to develop new avenues of human expression. And so today we are proud to announce NSynth (Neural Synthesizer), a novel approach to music synthesis designed to aid the creative process.

Unlike a traditional synthesizer which generates audio from hand-designed components like oscillators and wavetables, NSynth uses deep neural networks to generate sounds at the level of individual samples. Learning directly from data, NSynth provides artists with intuitive control over timbre and dynamics and the ability to explore new sounds that would be difficult or impossible to produce with a hand-tuned synthesizer.

The acoustic qualities of the learned instrument depend on both the model used and the available training data, so we are delighted to release improvements to both:

A full description of the dataset and the algorithm can be found in our arXiv paper.

Read More

Music, Art and Machine Intelligence (MAMI) Conference

By | machinelearning, ML, TensorFlow

This past June, Magenta, in parternship with the
Artists and Machine Intelligence group, hosted
the Music, Art and Machine Intelligence (MAMI) Conference in San Francisco.
MAMI brought together artists and researchers to share their work and explore
new ideas in the burgeoning space intersecting art and machine learning.

AMI has posted a wonderful summary
of the event on their blog, which we encourage you to read.

Many of the lectures have also been made available on YouTube,
including talks by Google ML researchers Samy Bengio
and Blaise Aguera y Arcas,
Wekinator creator Rebecca Fiebrink,
and artist Mario Klingmann.

We hope you will find the content of the conference as stimulating as we did
and take part in the ongoing conversation in our discussion group.

– Magenta

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Generating Long-Term Structure in Songs and Stories

By | machinelearning, ML, TensorFlow

One of the difficult problems in using machine learning to generate sequences, such as melodies, is creating long-term structure. Long-term structure comes very naturally to people, but it’s very hard for machines. Basic machine learning systems can generate a short melody that stays in key, but they have trouble generating a longer melody that follows a chord progression, or follows a multi-bar song structure of verses and choruses. Likewise, they can produce a screenplay with grammatically correct sentences, but not one with a compelling plot line. Without long-term structure, the content produced by recurrent neural networks (RNNs) often seems wandering and random.

But what if these RNN models could recognize and reproduce longer-term structure? Read More

Magenta MIDI Interface

By | machinelearning, ML, TensorFlow

The magenta team is happy to announce our first step toward providing an easy-to-use
interface between musicians and TensorFlow. This release makes it
possible to connect a TensorFlow model to a MIDI controller and synthesizer in
real time.

Don’t have your own MIDI keyboard? There are many free software
components you can download and use with our interface. Find out more details on
setting up your own TensorFlow-powered MIDI rig in the
README.

Read More

Data Science Weekly – Issue 176

By | machinelearning, TensorFlow

Data Science Weekly – Issue 176

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Curated news, articles and jobs related to Data Science. 
Keep up with all the latest developments

Issue #176

April 6 2017

Editor Picks

 

  • 'Reverse Prisma' AI turns Monet paintings into photos
    Impressionist art is more about feelings than realism, but have you ever wondered what Monet actually saw when he created pieces like Low Tide at Varengeville (above)? Thanks to researchers from UC Berkeley, you don't need to go to Normandy and wait for the perfect light. Using "image style transfer" they converted his impressionist paintings into a more realistic photo style…
  • DeepMind Solves AGI, Summons Demon
    This morning, a group of research scientists at Google DeepMind announced that they had inadvertently solved the riddle of artificial general intelligence (AGI). Their approach relies upon a beguilingly simple technique called symmetrically toroidal asynchronous bisecting convolutions. By the year’s end, Alphabet executives expect that these neural networks will exhibit fully autonomous self-improvement. What comes next may affect us all…

 


 

A Message from this week's Sponsor:

 

 


 

Data Science Articles & Videos

 

  • Catching Lottery Cheats with Data Science
    Recently I came across the paper “Statistics and the Ontario Lottery Retailer Scandal” written by Dr Jeffrey S. Rosenthal on a statistical study he carried out that uncovered a lottery fraud perpetrated by the clerks of retailer shops selling the lottery tickets at the expense of their clients…
  • Prediction Training
    Taking a cue from ML Twitch employees train to predict the future by predicting key outcomes from the past. 97% of Twitch staff recommend the training that inspired these exercises. See if you can predict key statistics about Silicon Valley, while you deliberately practice forecasting the future for the first time…
  • Nesterov's Punctuated Equilibrium
    Following the remarkable success of AlphaGo, there has been a groundswell of interest in reinforcement learning for games, robotics, parameter tuning, and even computer networking. In a landmark new paper by Salimans, Ho, Chen, and Sutskever from OpenAI, the authors show that a particular class of genetic algorithms (called Evolutionary Strategies) gives excellent performance on a variety of reinforcement learning benchmarks…
  • Improved Training of Wasserstein GANs
    Our method enables very stable GAN training: for the first time, we can train a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and language models over discrete data…
  • One-Shot Imitation Learning
    Imitation learning has been commonly applied to solve different tasks in isolation. This usually requires either careful feature engineering, or a significant number of samples. This is far from what we desire: ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering. In this paper, we propose a meta-learning framework for achieving such capability, which we call one-shot imitation learning…

 


 

Jobs

 

  • Senior Data Analyst – VSCO – Oakland, CA

    VSCO is a leading creative platform with a monthly audience of over 45 million and growing.

    We are looking for a Senior Data Analyst to build data at VSCO from the ground up. You will design our data model for user behavior and content impression, and will mine the data to find insights that will influence the product roadmap. Expect to get your hands dirty with Redshift, Spark, and data visualization tools under the guidance of our Director of Data Science…

 


 

Training & Resources

 

  • deepcolor
    Utilize the power of mathematical magic to automatically color a sketch or lineart…

  • The Best Resources for Learning D3.js
    If you are serious about becoming an expert in data visualization, there is one tool that has become increasingly popular over the past few years, and is essential to your repertoire…

 


 

Books

 

  • Linear Algebra and Its Applications

    "One of the best college textbooks ever! If I needed to miss a lecture, the book taught me everything I needed to know. I had no linear algebra background beforehand, and the book was very clear and easy to understand. Awesome for independent study."...

    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

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Multistyle Pastiche Generator

By | machinelearning, ML, TensorFlow

Vincent Dumoulin,
Jonathon Shlens,
and
Manjunath Kudlur
have extended image style transfer by creating a single network which performs
more than one stylization of an image. The
paper[1] has also been summarized
in a
Google
Research Blog
post. The source code and trained models behind the paper
are being released here.

The model creates a succinct description of a style. These descriptions can be
combined to create new mixtures of styles. Below is a picture of Picabo[5] stylized with a mixture of 3 different styles. Adjust the sliders below the
image to create more styles.

30%

35%

35%

var pasticheDemo = function(img_id, url_prefix) {

function getValue(index) {
return parseFloat(document.getElementById(img_id + ‘_style_’ + index).value);
}

function normalizeValues(values) {
var sum = values[0] + values[1] + values[2];
if (sum <= 0) {
return [0, 0, 0];
}
var normValue = function(v) {
return Math.round(v * 20 / sum) * 50;
};
var norm = [
normValue(values[0]), normValue(values[1]), normValue(values[2])];
sum = norm[0] + norm[1] + norm[2];
var diff = 1000 – sum;
var max = Math.max(norm[0], norm[1], norm[2]);
if (norm[0] == max) {
norm[0] += diff;
} else if (norm[1] == max) {
norm[1] += diff;
} else {
norm[2] += diff;
}
return norm;
}

function imageHash(values) {
var toString = function(v) {
var str = String(v);
while (str.length < 3) {
str = '0' + str;
}
// AdBlock Plus looks for patterns that match common ad image sizes.
// Breaking up the number with a character is enough to bypass this.
str = str[0] + 'a' + str.substr(1);
return str;
};
return toString(values[0]) + '_' + toString(values[1]) + '_' +
toString(values[2]);
}

function getImageUrl(prefix, values) {
href = '/assets/style_blends/' + prefix + '_' + imageHash(values) + '.jpg';
return href;
}

var preloadedImages = null;

function createImage(values) {
var img = new Image();
img.id = img_id;
var contents = {
'isloaded': false,
'image': img
};
img.onload = function() {
contents.isloaded = true;
};
img.src = getImageUrl(url_prefix, values);
return contents;
}

function getImage(values) {
var hash = imageHash(values);
var contents = preloadedImages[hash];
if (contents.isloaded) {
return contents.image;
} else {
preloadedImages[hash] = createImage(values);
}
return contents.image;
}

function loadAllImages() {
var images = {};
for (var x = 0; x <= 1000; x += 50) {
for (var y = 0; y <= 1000 – x; y += 50) {
for (var z = 0; z <= 1000 – x – y; z += 50) {
if (x + y + z == 1000 || x + y + z == 0) {
images[imageHash([x, y, z])] = createImage([x, y, z]);
}
}
}
}
return images;
}

function displayImage(image) {
var current = document.getElementById(img_id);
// Load the new image with the height of the current image so the slider
// stays in the same place.
image.width = current.width;
image.height = current.height;
var parent = current.parentElement;
parent.removeChild(current);
parent.appendChild(image);
}

function setWeightLabels(values) {
for (var index = 0; index < 3; ++index) {
var weight = document.getElementById(img_id + '_weight_' + index);
weight.innerHTML = (values[index] / 10) + '%';
}
}

function sliderChange() {
if (preloadedImages == null) {
preloadedImages = loadAllImages();
}

var img = document.getElementById(img_id);
var values = [getValue(0), getValue(1), getValue(2)];
var normalized = normalizeValues(values);
displayImage(getImage(normalized));
setWeightLabels(normalized);
}

document.getElementById(img_id + '_style_0').oninput = sliderChange;
document.getElementById(img_id + '_style_1').oninput = sliderChange;
document.getElementById(img_id + '_style_2').oninput = sliderChange;

};
pasticheDemo('picabo_deck', 'picabo');

Read More

Tuning Recurrent Neural Networks with Reinforcement Learning

By | machinelearning, ML, TensorFlow

We are excited to announce our new RL Tuner algorithm, a method for enchancing the performance of an LSTM trained on data using Reinforcement Learning (RL). We create an RL reward function that teaches the model to follow certain rules, while still allowing it to retain information learned from data. We use RL Tuner to teach concepts of music theory to an LSTM trained to generate melodies. The two videos below show samples from the original LSTM model, and the same model enchanced using RL Tuner.

Read More