KDnuggets™ News 18:n41, Oct 31: Introduction to Deep Learning with Keras; Easy Named Entity Recognition with Scikit-Learn
Also: Generative Adversarial Networks - Paper Reading Road Map; How I Learned to Stop Worrying and Love Uncertainty; Implementing Automated Machine Learning Systems with Open Source Tools; Notes on Feature Preprocessing: The What, the Why, and the How
Features | Tutorials | Opinions | Reports | News | Webcasts | Courses | Jobs | Academic | Tops | Image of the week
Features
Tutorials, Overviews
Opinions
Reports
News
Webcasts and Webinars
Courses, Education
Jobs
Academic
Top Stories, Tweets
Image of the week
This past week on KDnuggets we shared a roadmap to learning GANs, offered up an introductory tutorial using Keras for deep learning, learned how to perform named entity recognition with common Python tools, got certain about uncertainty, saw consistency in poll results on the largest datasets our readers analyzed, and learned about open source automated machine learning tools. Don't forget to participate in the latest KDnuggets poll, How Important is Understanding Machine Learning Models?
Features
- Introduction to Deep Learning with Keras
- Generative Adversarial Networks - Paper Reading Road Map
- New Poll: How Important is Understanding Machine Learning Models?
Named Entity Recognition and Classification with Scikit-Learn
- How I Learned to Stop Worrying and Love Uncertainty
- Amazing consistency: Largest Dataset Analyzed / Data Mined - Poll Results and Trends
- Implementing Automated Machine Learning Systems with Open Source Tools
Tutorials, Overviews
- Notes on Feature Preprocessing: The What, the Why, and the How
- Are you buying an apartment? How to hack competition in the real estate market
- Naive Bayes from Scratch using Python only - No Fancy Frameworks
- Building a Question-Answering System from Scratch
Opinions
- Stop Installing Tensorflow Using pip for Performance Sake!
- Top Obstacles to Overcome when Implementing Predictive Maintenance
- AI Masterpieces: But is it Art?
Reports
- Key Takeaways from AI Conference SF, Day 2: AI and Security, Adversarial Examples, Innovation
- Key Takeaways from AI Conference SF, Day 1: Domain Specific Architectures, Emerging China, AI Risks
News
- How to Mitigate Open Source License Risks
SQL, Python, & R in One Platform
- New Book: Linear Algebra - what you need for Machine Learning and Data Science now
- [ebook] Manipulating Data in Apache Spark
Webcasts and Webinars
Courses, Education
Jobs
- Moody's Analytics: Machine Learning / NLP - Research Scientist / Engineer [New York, NY]
- American Association of Colleges of Osteopathic Medicine: Data Analyst [Bethesda, Maryland]
- Bank of Canada: Data Scientist [Ottawa, Canada]
- NAIC: Analyst I (Capital Markets) [New York, NY]
Academic
- Lehigh University: Tenure Track Positions in Foundations of Data Science [Bethlehem, PA]
- Monash University: Academic Opportunities in Dialogue Research [Melbourne, Australia]
- U. of Zurich: Assistant Professorship in AI and Machine Learning (Non-tenure Track) [Zurich, Switzerland]
- U. of Zurich: Professorship in Big Data Science (Open Rank) [Zurich, Switzerland]
Top Stories, Tweets
- Top Stories, Oct 22-28: 9 Must-have skills you need to become a Data Scientist, updated; Named Entity Recognition and Classification with Scikit-Learn
- Top KDnuggets tweets, Oct 17-23: Machine Learning Cheat Sheets
Image of the week
From Notes on Feature Preprocessing: The What, the Why, and the How
