Oct
2017 Nov Top Stories, Tweets
All (102) | Courses, Education (7) | Meetings (9) | News, Features (9) | Opinions, Interviews (24) | Top Stories, Tweets (10) | Tutorials, Overviews (36) | Webcasts & Webinars (7)
- Top KDnuggets tweets, Nov 22-28: Reinforcement Learning: An Introduction by Sutton and Barto – Complete Second Draft
- Nov 29, 2017.
Also #DeepLearning Specialization by Andrew Ng - 21 Lessons Learned; How (and Why) to Create a Good Validation Set; Predicting Cryptocurrency Prices With #DeepLearning
- Top KDnuggets tweets, Nov 15-21: DeepLearning is “shallow”: here are underlying concepts you need
- Nov 27, 2017.
Also: New Poll: Data Science / Machine Learning methods you used; The amazing predictive power of conditional probability in Bayes Nets; The 10 Statistical Techniques Data Scientists Need to Master.
- Top Stories, Nov 20-26: Deep Learning Specialization by Andrew Ng – 21 Lessons Learned; A Framework for Approaching Textual Data Science Tasks
- Nov 27, 2017.
Also: Estimating an Optimal Learning Rate For a Deep Neural Network; Automated Feature Engineering for Time Series Data; How (and Why) to Create a Good Validation Set; Building a Wikipedia Text Corpus for Natural Language Processing; The 10 Statistical Techniques Data Scientists Need to Master
- 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
- Nov 20, 2017.
Also: A Day in the Life of a Data Scientist; Top 10 Videos on Deep Learning in Python; 8 Ways to Improve Your Data Science Skills in 2 Years; Machine Learning Algorithms: Which One to Choose for Your Problem; Top 10 Machine Learning Algorithms for Beginners
- Top KDnuggets tweets, Nov 08-14: Approaching (Almost) Any NLP Problem on #Kaggle; Choosing an Open Source #MachineLearning Library
- Nov 15, 2017.
Also: What is the difference between Bagging and Boosting?; Which #Python package manager should you use?; The Practical Importance of Feature Selection.
- Top Stories, Nov 6-12: When Will Demand for Data Scientists/Machine Learning Experts Peak?; Interpreting Machine Learning Models: An Overview
- Nov 13, 2017.
Also: TensorFlow: What Parameters to Optimize?; 7 Super Simple Steps From Idea To Successful Data Science Project; Tips for Getting Started with Text Mining in R and Python; Top 10 Machine Learning Algorithms for Beginners
- Top KDnuggets tweets, Nov 01-07: Airbnb develops an #AI which converts design into source code
- Nov 8, 2017.
Also: One LEGO at a time: Explaining the #Math of How #NeuralNetworks Learn; 6 Books Every #DataScientist Should Keep Nearby; Direct from Sebastian Raschka #Python #MachineLearning book, new edition.
- Top October Stories: Top 10 Machine Learning Algorithms for Beginners
- Nov 8, 2017.
Also: Understanding Machine Learning Algorithms; Want to Become a Data Scientist? Read This Interview First; 6 Books Every Data Scientist Should Keep Nearby.
- Top Stories, Oct 30-Nov 5: 6 Books Every Data Scientist Should Keep Nearby; Want to know how Deep Learning works? Here’s a quick guide for everyone.
- Nov 6, 2017.
Also: Advice For New and Junior Data Scientists; 7 Steps to Mastering Deep Learning with Keras; Getting Started with Machine Learning in One Hour!; Top 10 Machine Learning Algorithms for Beginners
- Top KDnuggets tweets, Oct 25-31: 30 Essential Data Science, Machine Learning, Deep Learning Cheat Sheets; Google Brain chief: DL takes at least 100,000 examples
- Nov 1, 2017.
Also Applied #AI Summit will give you the tools for your AI journey, 5-7 Feb, London;10 Free Must-Read Books for Machine Learning, Data Science; Ranking Popular #DeepLearning Libraries for #DataScience.
Top 9 Mobile Apps for Learning and Practicing Data Science