Topic
A/B Testing (9) API (1) Artificial Intelligence (31) Automation (6) Automattic (20) best practices (10) Bias in AI (6) Books and Reading (6) Career and Professional Development (14) Causal Inference (1) Communication (14) Conferences and Events (5) Data Analytics (23) Data Discoverability (4) Data Engineering (12) Data Ethics (10) Data Products (1) Data Science (53) Data Speaker Series (4) Data Visualization (23) Deep Learning (3) Distributed Work (8) Diversity and Inclusion (8) Elasticsearch (8) Experimentation (12) Information Retrieval (6) Learning and Education (6) Machine Learning (43) Marketing Analytics (7) Meetup (2) Natural Language Processing (6) Network Analysis (8) Open Source (7) Productivity (10) Python (8) Remote Work (21) Scientific Communication (10) Semantic Search (2) Social Media Analytics (1) Software Engineering (4) Surveys and Research Methods (1) Tech Industry (14) Technology Trends (1) Time Series Analysis (2) Transparency in Data (2) WordPress (21) WordPress.com (1) Work Life Balance (6)
This week, Boris, Xiao, and Carly share recent reads about MOOCs, collusion in AI pricing, and generating fake news with artificial intelligence.
Demet takes you deep into pipe, a tool that allows anyone at Automattic to build solid machine learning models.
This week, Charles shares a couple of talks he enjoyed on neural networks at Georgia Tech’s Theoretical Foundation of Deep Learning conference.
Don’t forget to share your favorite reads in the data science field!
Here are our favorite talks from Spark + AI Summit 2018