InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
-
Understanding Deep Learning
Jessica Yung talks about the foundational concepts about neural networks and highlights key things to pay attention to: learning rates, how to initialize a network, and more.
/presentations/understanding-deep-learning/en/mediumimage/Jessica-Yung-m-1558000059616.jpg)
-
Intuition & Use-Cases of Embeddings in NLP & beyond
Jay Alammar talks about the concept of word embeddings, how they're created, and looks at examples of how these concepts can be carried over to solve problems.
/presentations/nlp-word-embedding/en/mediumimage/QCon-Jay-Alammar-m-1558000454598.jpg)
-
How to Prevent Catastrophic Failure in Production ML Systems
Martin Goodson describes the unpredictable nature of artificial intelligence systems and how mastering a handful of engineering principles can mitigate the risk of failure.
/presentations/ml-systems-failure-production/en/mediumimage/QCon-Martin-Goodson-m-1558007869358.jpg)
-
Productionizing H2O Models with Apache Spark
Jakub Hava demonstrates the creation of pipelines integrating H2O machine learning models and their deployments using Scala or Python.
/presentations/h2o-model-spark/en/mediumimage/Jacbig-1557538121108.JPG)
-
YugaByte DB - A Planet-Scale Database for Low Latency Transactional Apps
Amey Banarse and Karthik Ranganathan introduce and demo YugaByte DB, a large scale DB, highlighting distributed transactions with global consistency.
/presentations/yugabytedb/en/mediumimage/Ambig-1556751276232.JPG)
-
Test-Driven Machine Learning
Detlef Nauck explains why the testing of data is essential, as it not only drives the machine learning phase itself, but it is paramount for producing reliable predictions after deployment.
/presentations/tdd-ml/en/mediumimage/Qcon-London-Detlef-Nauck-m-1556177499049.jpg)
-
Data Science for Lazy People, Automated Machine Learning
Diego Hueltes discusses using Automated Machine Learning as a personal assistant in Data Science.
/presentations/automated-ml-assistant-ds/en/mediumimage/Diebig-1555887468890.JPG)
-
Winning Ways for Your Visualization Plays
Mark Grundland explores practical techniques for information visualization design to take better account of the fundamental limitations of visual perception.
/presentations/information-visualization-techniques/en/mediumimage/Markbig-1555377131663.JPG)
-
Open Source Robotics: Hands on with Gazebo and ROS 2
Louise Poubel gives an overview of ROS (Robot Operating System) and Gazebo (a multirobot simulator), the problems they've been solving so far and what's on the roadmap for the future.
/presentations/gazebo-ros-2/en/mediumimage/Louise-Poubel-m-1556811393905.jpg)
-
Building Artificial General Intelligence
Peter Morgan takes a look at how deep learning is presently being extended in ways that take AI technologies far beyond the simple image classifiers that they were originally developed to solve.
/presentations/general-ai-ml/en/mediumimage/Peter-Morgan-m-1554390277281.jpeg)
-
Jupyter Notebooks: Interactive Visualization Approaches
Chakri Cherukuri talks about how to understand and visualize machine learning models using interactive widgets and introduces the widget libraries.
/presentations/ml-models-jupyter/en/mediumimage/Chakri-Cherukuri-m-1554358378987.jpg)
-
Fairness, Transparency, and Privacy in AI @LinkedIn
Krishnaram Kenthapadi focuses on the application of privacy-preserving data mining and fairness-aware ML techniques in practice, by presenting case studies spanning different LinkedIn applications.
/presentations/linkedin-privacy-ml/en/mediumimage/Krishnaram-Kenthapadi-m-1554224253811.jpg)