2016 Jul News, Features
All (112) | Courses, Education (13) | Meetings (8) | News, Features (17) | Opinions, Interviews, Reports (31) | Software (8) | Tutorials, Overviews (31) | Webcasts & Webinars (4)
- Top KDnuggets tweets, Jul 20-26: Math-free simple explanation: #DeepLearning Demystified; Are #Humans Becoming More Machine-Like? - Jul 27, 2016.
Finally, a #TensorFlow book for humans; Great math-free simple intro explanation video: Deep Learning Demystified; Does #sentiment analysis work? A tidy analysis of Yelp reviews; JupyterLab: the next generation of the #Jupyter Notebook
- Top Stories, July 18-24: Why Big Data is in Trouble; In Deep Learning, Architecture Engineering is the New Feature Engineering - Jul 25, 2016.
Why Big Data is in Trouble: They Forgot About Applied Statistics; In Deep Learning, Architecture Engineering is the New Feature Engineering; 5 Big Data Projects You Can No Longer Overlook; What Has Pokemon Got To Do With Big Data?
- SAS vs R vs Python: Which Tool Do Analytics Pros Prefer? - Jul 22, 2016.
There are lots of flame wars involving different data science and analytics tools... but this isn't one of them. Check out the quantitative results and analysis of a Burtch Works survey on the subject.
- Top KDnuggets tweets, Jul 13 – Jul 19: Bayesian #MachineLearning, Explained; Introducing JupyterLab - Jul 20, 2016.
Bayesian #MachineLearning, Explained; JupyterLab: the next generation of the #Jupyter Notebook; On the importance of democratizing #ArtificialIntelligence
- 10 Great Talks From SciPy 2016 - Jul 20, 2016.
Here's a curated short list of interesting and insightful talks to watch from SciPy 2016 to help guide your search through the volume of great video material emerging from the conference.
- Top Stories, July 11–17: Top Machine Learning MOOCs and Online Lectures; Bayesian Machine Learning, Explained - Jul 18, 2016.
Top Machine Learning MOOCs and Online Lectures; Bayesian Machine Learning, Explained; 10 Algorithm Categories for A.I., Big Data, and Data Science; 5 Deep Learning Projects You Can No Longer Overlook; The Hard Problems AI Can't (Yet) Touch
- Data Mining/Data Science “Nobel Prize”: 2016 SIGKDD Innovation Award to Philip S. Yu - Jul 15, 2016.
Dr. Philip S. Yu wins ACM KDD Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data.
- 2016 SIGKDD Service Award to Wei Wang - Jul 14, 2016.
Prof. Wei Wang wins ACM SIGKDD 2016 Service Award for her significant technical contributions to the principles, practice and application of data mining and for her outstanding services to society and the data mining community.
- Top KDnuggets tweets, Jul 6 – Jul 12: Statistical Data Analysis #Python #Jupyter Notebooks; Modern Pandas Notebooks - Jul 13, 2016.
Statistical Data Analysis in #Python (#Jupyter Notebooks); Modern Pandas: idiomatic Pandas notebook collection; New (free) book by @rdpeng: #rstats Programming for #DataScience
- TalkingData Data Science Competition: understand mobile users - Jul 12, 2016.
Unique opportunity to solve complex real world big data challenges for the China mobile market - predict users demographic characteristics based on their app usage, geolocation, and mobile device properties.
- Top Stories, July 4–10: The Invention of Support Vector Machines; Storytelling: The Power to Influence in Data Science - Jul 11, 2016.
Data Mining History: The Invention of Support Vector Machines; Storytelling: The Power to Influence in Data Science; Support Vector Machines: A Simple Explanation; Big Data, Bible Codes, and Bonferroni
- New Book: Effective CRM using Predictive Analytics – get 20% discount - Jul 11, 2016.
A comprehensive step-by-step guide to designing, setting up, executing and deploying data mining techniques in marketing. Use code VBM93 for 20% discount.
- Top KDnuggets tweets, Jun 29 – Jul 5: Big Data Ecosystem is Too Damn Big!; Deep Learning Intro with Caffe and Python - Jul 6, 2016.
The #BigData Ecosystem is Too Damn Big!; A Practical Introduction to #DeepLearning with Caffe and #Python; What do Postgres, Kafka, and Bitcoin have in common?
- Top June stories: The Difference Deep Learning and “Regular” Machine Learning? R, Python duel as top Data Science tools. - Jul 5, 2016.
Also Data Science of Variable Selection; The Big Data Ecosystem is Too Damn Big.
- Top /r/MachineLearning Posts, June: Microsoft Videos, Machine Learning Training Pathway, Free Books! - Jul 5, 2016.
Microsoft Research Machine Learning Videos; Free Machine Learning Training Pathway; Andrew Ng's New Book; Coursera Removing Free Online Courses; Free Books!
- Academic/Research positions in Business Analytics, Data Science, Machine Learning in June 2016 - Jul 5, 2016.
Positions at Center for Data Science and Public Policy at U. Chicago; Business Analytics Lecturer at U. Iowa; IBM Social Good Fellow; Data quality postdoc at McMaster U; Asst. Prof. of Marketing at Yale, and more.
- Top Stories, June 27 – July 3: Big Data Ecosystem is Too Damn Big; 5 More Machine Learning Projects You Can’t Overlook - Jul 4, 2016.
The Big Data Ecosystem is Too Damn Big; 5 More Machine Learning Projects You Can No Longer Overlook; 7 Steps to Mastering Machine Learning With Python; Machine Learning Trends and the Future of Artificial Intelligence
![How to Select Rows and Columns in Pandas Using [ ], .loc, iloc, .at and .iat](https://www.kdnuggets.com/wp-content/uploads/panda-barbell.jpg)
Jun
