KD 17:n01: 5 Machine Learning Projects You Can’t Overlook; Future of Deep Learning
Gregory Piatetsky-Shapiro
![Gregory Piatetsky-Shapiro]()
Gregory Piatetsky-Shapiro
Part-time philosopher, Retired, Data Scientist, KDD and KDnuggets Founder, was LinkedIn Top Voice on Data Science & Analytics. Currently helping Ukrainian refugees in MA.
Features
- 5 Machine Learning Projects You Can No Longer Overlook, January
- Game Theory Reveals the Future of Deep Learning
- Get a data science job, guaranteed
- Generative Adversarial Networks - Hot Topic in Machine Learning
- Ten Myths About Machine Learning, by Pedro Domingos
- AI, Data Science, Machine Learning: Main Developments in 2016, Key Trends in 2017
- Predictive Analytics World for Manufacturing, Germany, Feb 2-3, Program highlights
- The Surprising Ethics of Humans and Self-Driving Cars
- Cartoon: When Self-Driving Car + Machine Learning takes you too far …
Tutorials, Overviews
- Internet of Things Tutorial: WSN and RFID - The Forerunners
- Sound Data Science: Avoiding the Most Pernicious Prediction Pitfall
- Creating Data Visualization in Matplotlib
- Tidying Data in Python
- 3 methods to deal with outliers
- Machine Learning and Cyber Security Resources
Opinions
- Text Mining Amazon Mobile Phone Reviews: Interesting Insights
- A Non-comprehensive List of Awesome Things Other People Did in 2016
- Social Media for Marketing and Healthcare: Focus on Adverse Side Effects
- arXiv Paper Spotlight: Sampled Image Tagging and Retrieval Methods on User Generated Content
- A Tasty approach to data science
- Machine Learning Meets Humans - Insights from HUML 2016
- The Major Advancements in Deep Learning in 2016
- How To Stay Competitive In Machine Learning Business
- Revenue per Employee: golden ratio, or red herring?
- Uber-fication! Uberize Your Business
- Citizen Data Scientist, Jumbo Shrimp, and Other Descriptions That Make No Sense
- Laying the Foundation for a Data Team
News
- KDnuggets Top Blogs and Bloggers in December 2016
- Top Stories, Jan 2-8: 5 Machine Learning Projects You Can No Longer Overlook, January; Machine Learning and Cyber Security Resources
- Top December Stories: 50+ Data Science, Machine Learning Cheat Sheets; Machine Learning/AI: Main 2016 Developments, Key 2017 Trends
- Top /r/MachineLearning Posts, December: OpenAI Universe; Deep Learning MOOC For Coders; Musk: Tesla Gets Awesome-er
- Top Stories, Dec 26-Jan 1: Game Theory Reveals the Future of Deep Learning; A Funny Look at Big Data and Data Science
- Academic/Research positions in Business Analytics, Data Science, Machine Learning in December 2016
Courses, Education
- Prepare for Growing Data Field with Merrimack College
- Big Data to Big Profits: Strategies for Monetizing Social, Mobile, and Digital Data with Data Science, Mar 23-24, San Francisco
- Fundamentals of Machine Learning for Predictive Data Analytics, Dublin, 21-23 March, 2017
Meetings
- Data Analytics Summit, March 9-10, Free Event - RSVP
- Predictive Analytics World for Manufacturing, Germany, Feb 2-3, Program highlights
- What Are Your Analytics Goals for 2017? Get a head start at TDWI Las Vegas, Feb 12-17
- Deep Learning Summit in San Francisco, Jan 26-27 (KDnuggets Offer)
- 100+ Upcoming Meetings in Analytics, Big Data, Data Mining, Data Science: January and Beyond
- Supercharge Your Data Science Team with AnacondaCON Team Discount, till Jan 16
- Global Predictive Analytics and Data Management Forum, Milan, February 2-3, 2017
Jobs
- Amazon IISc Young Scientist Big Data Fellowship, Bangalore
- Potrero Medical: Medical Device Data Scientist
Academic
- Syracuse University, School of Information Studies: Open Rank Faculty Position Data Science
- U. of Notre Dame: Academic Director Data Science Online MS & Special Professional Faculty
Publications
Top Tweets
Read also on KDnuggets
https://www.kdnuggets.com/2017/n01.html
Machine Learning Applied Research
7 年Great overview Gregory. There's work done by Physicists on Minority Games (MG) on dynamical systems over the last decade or so. MG superseded Nash Equilibrium. MG can also replicate the stylistic effects of macroeconomic theory which mainstream economic's theories had failed to do so. As far as I know, there's never been any successful application of any types of machine learning to model micro/macro-economics stylistic dynamics at all. So, I have my doubts about this new work on Nash-Equilibrium with Adversarial Networks if it can reproduce the results of MG, which it has been confirmed. "Minority Games : What happens when physicists start doing economics" https://news.softpedia.com/news/Minority-Games-38625.shtml