2017 Nov Opinions, Interviews

• InfoGAN - Generative Adversarial Networks Part III - Nov 30, 2017.
  In this third part of this series of posts the contributions of InfoGAN will be explored, which apply concepts from Information Theory to transform some of the noise terms into latent codes that have systematic, predictable effects on the outcome.

  Deep Learning, Generative Adversarial Network, Neural Networks, TensorFlow

• Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras - Nov 29, 2017.
  We show how to build a deep neural network that classifies images to many categories with an accuracy of a 90%. This was a very hard problem before the rise of deep networks and especially Convolutional Neural Networks.

  Convolutional Neural Networks, Deep Learning, Keras, TensorFlow

• Survival Analysis for Business Analytics - Nov 27, 2017.
  We compare survival analysis to other predictive techniques, and provide examples of how it can produce business value, with a focus on Kaplan-Meier and Cox Regression methods which have been underutilized in business analytics.

  Business Analytics, Survival Analysis, Time Series

• Are Scientists Doing Too Much Research? - Nov 24, 2017.
  At the heart of this reproducibility problem is the statistical inference methods used to validate research findings—specifically the concept of “statistical significance.”

  Galit Shmueli, John Ioannidis, Overfitting, Research

• Did Spark Really Kill Hadoop? - Nov 22, 2017.
  A comprehensive survey conducted by iDatalabs shows us the trends of the future of these two Data Science technologies.

  Apache Spark, Big Data, Hadoop, iDatalabs

• Using TensorFlow for Predictive Analytics with Linear Regression - Nov 21, 2017.
  This post presents a powerful and simple example of how to use TensorFlow to perform a Linear Regression. check out the code for your own experiments!

  Linear Regression, TensorFlow

• Key Takeaways from Open Data Science Conference (ODSC) West 2017 - Nov 21, 2017.
  This year, the ODSC West was held at the Hyatt Regency San Francisco Airport, from November 2 to 4. I am, attempting here, to give you a snapshot tour of what I experienced.

  AI, CA, Conference, Data Science, Deep Learning, DJ Patil, Machine Learning, Neural Networks, ODSC, San Francisco

• How (& Why) Data Scientists and Data Engineers Should Share a Platform - Nov 17, 2017.
  Sharing one platform has some obvious benefits for Data Science and Data Engineering teams, but technical, language and process challenges often make this a challenge. Learn how one company implemented single cloud platform for R, Python and other workloads – and some of the unexpected benefits they discovered along the way.

  Apache Spark, Cazena, Data Science Platform, Hadoop, Python, R

• Stop Doing Fragile Research - Nov 17, 2017.
  If you develop methods for data analysis, you might only be conducting gentle tests of your method on idealized data. This leads to “fragile research,” which breaks when released into the wild. Here, I share 3 ways to make your methods robust.

  Advice, Career, Data Science Skills, Hyperparameter, Overfitting, Research

• The Python Graph Gallery - Nov 16, 2017.
  Welcome to the Python Graph Gallery, a website that displays hundreds of python charts with their reproducible code snippets.

  Data Visualization, Matplotlib, Python, Seaborn

• You have created your first Linear Regression Model. Have you validated the assumptions? - Nov 15, 2017.
  Linear Regression is an excellent starting point for Machine Learning, but it is a common mistake to focus just on the p-values and R-Squared values while determining validity of model. Here we examine the underlying assumptions of a Linear Regression, which need to be validated before applying the model.

  Data Science, Linear Regression, Machine Learning, Multicollinearity, Statistics

• Some Things to Remember About Memory - Nov 14, 2017.
  A lot of the recent buzz about memory is old news.

  Brain, Marketing, Research

• The amazing predictive power of conditional probability in Bayes Nets - Nov 13, 2017.
  This article explains how Bayes Nets gain remarkable predictive power by their use of conditional probability. This adds to several other salient strengths, making them a preeminent method for prediction and understanding variables’ effects.

  Bayesian, Bayesian Networks, Monty Hall, Predictive Models

• A Day in the Life of a Data Scientist - Nov 13, 2017.
  Are you interested in what a data scientist does on a typical day of work? Each data science role may be different, but these five individuals provide insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.

  Advice, Career, Data Science, Data Scientist

• Overview of GANs (Generative Adversarial Networks) – Part I - Nov 10, 2017.
  A great introductory and high-level summary of Generative Adversarial Networks.

  Deep Learning, GANs, Generative Adversarial Network, Neural Networks

• 2018 Will Be the Perfect Time to Build an AI Startup - Nov 10, 2017.
  At the core, AI is actually built into many technologies currently in use, and it’s probably not as risky an investment as you might think.

  AI, Startup

• How Bayesian Networks Are Superior in Understanding Effects of Variables - Nov 9, 2017.
  Bayes Nets have remarkable properties that make them better than many traditional methods in determining variables’ effects. This article explains the principle advantages.

  Bayesian, Bayesian Networks, Predictive Models, Probability, Regression, Statistics

• Data Scientist: The Hottest Job on Wall Street - Nov 8, 2017.
  The demand for professionals that can build financial analytics programs is booming. We foresee two main objectives- to predict market movement for profit, and to protect customer assets of banks.

  AI, Cybersecurity, Data Scientist, Finance, Fintech, Fraud Prevention, Stocks

• Choosing an Open Source Machine Learning Library: TensorFlow, Theano, Torch, scikit-learn, Caffe - Nov 8, 2017.
  Open Source is the heart of innovation and rapid evolution of technologies, these days. Here we discuss how to choose open source machine learning tools for different use cases.

  Caffe, Machine Learning, Open Source, scikit-learn, TensorFlow, Theano, Torch

• How to Job Interview a Data Scientist - Nov 7, 2017.
  Data Scientist is a very broad term and hiring a good fit data scientist for your project is challenging task. Here we discuss this important topic in details.

  Data Science Skills, Data Scientist, Hiring, Mode Analytics, Skills, Venn Diagram

• What is the difference between Bagging and Boosting? - Nov 6, 2017.
  Bagging and Boosting are both ensemble methods in Machine Learning, but what’s the key behind them? Here we explain in detail.

  Bagging, Boosting, Ensemble Methods, Machine Learning

• More than the Hype: Beyond Gartner’s Hype Cycle - Nov 3, 2017.
  Gartner publishes hype cycles across different technologies and sectors. Here we conduct detailed analysis of Gartner’s Hype Cycles.

  Gartner, Hype, Stocks

• Machine Ethics and Artificial Moral Agents - Nov 2, 2017.
  This article is simply a stream of consciousness on questions and problems I have been thinking and asking myself, and hopefully, it will stimulate some discussion.

  Agents, AI, Bias, Ethics, Machine Intelligence, Responsible AI

• Advice For New and Junior Data Scientists - Nov 2, 2017.
  This article is for people who are already in the field but are just starting out. My goal is to not only use this post as a reminder to myself about the important things that I have learned, but also to inspire others as they embark onto their DS careers!

  Advice, AirBnB, Career, Data Science, Data Scientist