KDnuggets™ News 16:n26, Jul 20: Bayesian Machine Learning, Explained; Start Learning Deep Learning; Big Data is in Trouble
Bayesian Machine Learning, Explained; How to Start Learning Deep Learning; Why Big Data is in Trouble: They Forgot About Applied Statistics; Data Mining/Data Science "Nobel Prize": 2016 SIGKDD Innovation Award to Philip S. Yu
Features | Software | Tutorials | Opinions | News | Webcasts | Courses | Meetings | Quote
Features
Bayesian Machine Learning, Explained
- How to Start Learning Deep Learning
- Why Big Data is in Trouble: They Forgot About Applied Statistics
- Data Mining/Data Science "Nobel Prize": 2016 SIGKDD Innovation Award to Philip S. Yu
- 2016's Best Places for Data Scientist Jobs
- KDnuggets Interview: Inderpal Bhandari, IBM Global Chief Data Officer on 4 key ideas of Cognitive Computing
Bayesian Machine Learning, ExplainedSoftware
Tutorials, Overviews, How-Tos
- Predictive Analytics Introductory Key Terms, Explained
- In Deep Learning, Architecture Engineering is the New Feature Engineering
- Statistical Data Analysis in Python
- America's Next Topic Model
- MNIST Generative Adversarial Model in Keras
Opinions
- 10 Algorithm Categories for A.I., Big Data, and Data Science
- What Data Scientists Can Learn From Qualitative Research
- Data Mining Most Vexing Problem Solved, or is this drug REALLY working?
- 4 Major Trends Disrupting the Data Science Market
- What the Next Generation of IoT Sensors Have in Store
- What do Postgres, Kafka, and Bitcoin Have in Common?
News
- Top Stories, July 11-17: Top Machine Learning MOOCs and Online Lectures; Bayesian Machine Learning, Explained
- Top KDnuggets tweets, Jul 6 - Jul 12: Statistical Data Analysis #Python #Jupyter Notebooks; Modern Pandas Notebooks
- 2016 SIGKDD Service Award to Wei Wang
Webcasts and Webinars
Courses
- Online Master of Science in Predictive Analytics
- Online Courses: Big Data Projects and Data Science Pipelines
- Metis Data Science Open Houses: San Francisco and New York City
Meetings
Quote
"In God we trust. All others must bring data." W. Edwards Deming
