KDnuggets™ News 17:n45, Nov 29: New Poll: Data Science Methods Used? Deep Learning Specialization: 21 Lessons Learned

Also The 10 Statistical Techniques __data Science. Features |  Software |  Tutorials |  Opinions |  Tops |  News |  Webcasts |  Courses |  Meetings |  Jobs |  Academic |  Image of the week

  • New Poll: Which data Science / Machine Learning methods and tools you used?
  • Gold Blog
    Deep Learning Specialization by Andrew Ng - 21 Lessons Learned
  • Gold Blog
    The 10 Statistical Techniques data Scientists Need to Master
  • Did Spark Really Kill Hadoop?
  • Silver Blog
    A Framework for Approaching Textual data Science Tasks
  • Automated Feature Engineering for Time Series Data
  • Best Masters in data Science and Analytics in US/Canada
  • Cartoon: Thanksgiving, Big Data, & Turkey data Science.

  • Natural Language Processing Library for Apache Spark - free to use
  • The Python Graph Gallery
  • PySpark SQL Cheat Sheet: Big data in Python
  • NVIDIA DGX Systems - Deep Learning Software Whitepaper

  Tutorials, Overviews
  • How To Unit Test Machine Learning Code
  • Analyzing the Migration of Scientific Researchers
  • How (and Why) to Create a Good Validation Set
  • Understanding Objective Functions in Neural Networks
  • Building a Wikipedia Text Corpus for Natural Language Processing
  • Estimating an Optimal Learning Rate For a Deep Neural Network
  • Generative Adversarial Networks - Part II
  • Top 10 Videos on Deep Learning in Python
  • 8 Ways to Improve Your data Science Skills in 2 Years
  • Capsule Networks Are Shaking up AI - Here's How to Use Them
  • Basic Concepts of Feature Selection

  • Survival Analysis for Business Analytics
  • Are Scientists Doing Too Much Research?
  • Using TensorFlow for Predictive Analytics with Linear Regression
  • Key Takeaways from Open data Science Conference (ODSC) West 2017
  • How (& Why) data Scientists and data Engineers Should Share a Platform
  • Stop Doing Fragile Research
  • You have created your first Linear Regression Model. Have you validated the assumptions?

  Top Stories, Tweets
  • Top Stories, Nov 20-26: Deep Learning Specialization by Andrew Ng - 21 Lessons Learned; A Framework for Approaching Textual data Science Tasks
  • Top Stories, Nov 13-19: The 10 Statistical Techniques data Scientists Need to Master; Best Online Masters in data Science and Analytics - a comprehensive, unbiased survey
  • Top KDnuggets tweets, Nov 15-21: DeepLearning is "shallow": here are underlying concepts you need
  • Top KDnuggets tweets, Nov 08-14: Approaching (Almost) Any NLP Problem on #Kaggle; Choosing an Open Source #MachineLearning Library

  • [eBook] A Gentle Introduction to Apache Spark(tm)
  • Call for Bids to Host KDD-202x
  • DataScience.com Adds Former U.S. Chief data Scientist DJ Patil to Advisory Board

  Webcasts and Webinars
  • Webinar: data Preparation Essentials for Automated Machine Learning, Nov 29
  • Taming the Python Visualization Jungle, Nov 29 Webinar
  • Multichannel Marketing Attribution with Automated Machine Learning, Dec 12 Webinar
  • Fusing Human and Machine for Seamless, Automated Insurance Claims (Webinar, Dec 14)

  Courses, Education
  • Implementing Enterprise AI course using TensorFlow and Keras
  • A Course in Semantic Technologies for Designing a Proof-of-Concept, starting Nov 30

  • Chief data & Analytics Officer Sydney, Mar 20-22, KDnuggets Offer
  • We Speak data at TDWI Las Vegas, Feb 11-16. Save w. code KD30 thru Dec 15
  • Deep Learning in Robotics and Healthcare Summits: Join & save with KDnuggets offer

  • The Humanalysts: data Analytics Team, 9
  • Apple: Machine Learning-Industrial Methods Engineer
  • American Family Insurance: Director, data Science & Analytics
  • HelloFresh: Machine Learning Engineer
  • HelloFresh: Big data Engineer
  • HelloFresh: Senior data Scientist

  • USF data Institute: Postdoc
  • Virginia Tech: Assistant Professors in data Analytics
  • IESEG School of Management: Assistant, Associate or Full Professor in Marketing Analytics

   Image of the week
Scale drives Deep Learning Progress