2017 Jun News, Features
All (100) | Courses, Education (10) | Meetings (11) | News, Features (13) | Opinions, Interviews (26) | Software (6) | Tutorials, Overviews (31) | Webcasts & Webinars (3)
- Top KDnuggets tweets, Jun 21-27: An Introduction to Key #DataScience Concepts; Emerging #BigData #DeepLearning #Python Ecosystem - Jun 28, 2017.
Also 5 #EBooks to Read Before Getting into #DataScience; Awesome Public Datasets on GitHub; The Data Science Process, Rediscovered.
- Top Stories, Jun 19-25: Emerging Ecosystem: Data Science & Machine Learning Software, Analyzed; Machine Learning Algorithms in Self-Driving Cars - Jun 26, 2017.
Emerging Ecosystem: Data Science and Machine Learning Software, Analyzed; The Machine Learning Algorithms Used in Self-Driving Cars; The world’s first protein database for Machine Learning and AI; Making Sense of Machine Learning; 75 Big Data Terms to Know to Make your Dad Proud
- Top KDnuggets tweets, Jun 14-20: 5 EBooks to Read Before Getting into A Data Science or Big Data Career - Jun 21, 2017.
Also 10 Free Must-Read Books for #MachineLearning and #DataScience; #Keras implementation of a simple Neural Net module for relational reasoning; Applying #deeplearning to real-world problems
- Top Stories, Jun 12-18: Top 15 Python Libraries for Data Science in 2017; Deep Learning Papers Reading Roadmap - Jun 19, 2017.
Top 15 Python Libraries for Data Science in 2017; Deep Learning Papers Reading Roadmap; The Practical Importance of Feature Selection; Understanding Deep Learning Requires Re-thinking Generalization; K-means Clustering with Tableau
- Top KDnuggets tweets, Jun 07-13: Is Regression Analysis Really Machine Learning? - Jun 14, 2017.
Machine Learning in Real Life: Tales from the Trenches; Is Regression Analysis Really Machine Learning?; Implementing Your Own k-Nearest Neighbour Algorithm Using Python; Building Simple Neural Networks - TensorFlow for Hackers.
- Top May Stories: KDnuggets Poll: Software for Analytics, Data Science, Machine Learning; How to Learn Machine Learning in 10 Days - Jun 13, 2017.
Also Machine Learning Workflows in Python from Scratch Part 1: Data Preparation; Deep Learning - Past, Present, and Future
- Data Mining Techniques, Free Chapter: Derived Variables – Making the Data Mean More - Jun 12, 2017.
Download this chapter by Gordon Linoff and Michael Berry, and learn how to create derived variables, which allow the statistical modeling process to incorporate human insights.
- Top Stories, Jun 5-11: Is Regression Analysis Really Machine Learning?; 6 Interesting Things You Can Do with Python on Facebook Data - Jun 12, 2017.
Is Regression Analysis Really Machine Learning?; 6 Interesting Things You Can Do with Python on Facebook Data; A Practical Guide to Machine Learning; K-means Clustering with R: Call Detail Record Analysis; Machine Learning in Real Life: Tales from the Trenches to the Cloud
- Top /r/MachineLearning Posts, May: Deep Image Analogy; Stylized Facial Animations; Google Open Sources Sketch-RNN - Jun 9, 2017.
Deep Image Analogy; Example-Based Synthesis of Stylized Facial Animations; Google releases dataset of 50M vector drawings, open sources Sketch-RNN implementation; New massive medical image dataset coming from Stanford; Everything that Works Works Because it's Bayesian: Why Deep Nets Generalize?
- Prepare Your Organization for Smooth AI Adoption - Jun 8, 2017.
Download this free whitepaper on how to prepare your company for all the challenges that it may face on the way to data-driven prosperity.
- Top KDnuggets tweets, May 31-Jun 6: Essential Cheat Sheets for #MachineLearning and #DeepLearning - Jun 7, 2017.
The Artificial #ArtificialIntelligence Bubble and the Future of #Cybersecurity; Which #MachineLearning #Algorithm Should I Use? A handy #cheatsheet; 50 Companies Leading The #AI Revolution, Detailed; #MachineLearning Workflows in #Python from Scratch Part 1: Data Preparation
- TPOT Automated Machine Learning Competition: Can AutoML beat humans on Kaggle? - Jun 5, 2017.
Over the next couple months, we’re going to challenge you to apply TPOT to any data science problem you find interesting on Kaggle. If your entry ranks in the top 25% of the leaderboard on a Kaggle problem, we want to see how TPOT helped you accomplish that.
- Top Stories, May 29-Jun 4: Machine Learning Workflows in Python from Scratch; Machine Learning Algorithms Cheat Sheet - Jun 5, 2017.
Machine Learning Workflows in Python from Scratch Part 1: Data Preparation; Which Machine Learning Algorithm Should I Use?; 7 Steps to Mastering Data Preparation with Python; 7 Techniques to Handle Imbalanced Data; Why Does Deep Learning Not Have a Local Minimum?