InfoQ Homepage Machine Learning Content on InfoQ
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Computer Mathematics, AI and Functional Programming
Moa Johansson discusses the history of computer mathematics and how it connects to the development of early functional programming languages like Standard ML.
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Evoking Magic Realism with Augmented Reality Technology
Diana Hu explores how building a real world system is more a software engineering art, requiring making choices among a set of tradeoffs.
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MLflow: An Open Platform to Simplify the Machine Learning Lifecycle
Corey Zumar offers an overview of MLflow – a new open source platform to simplify the machine learning lifecycle from Databricks.
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Getting Started in Deep Learning with TensorFlow 2.0
Brad Miro explains what deep learning is, why one may want to use it over traditional ML methods, as well as how to get started building deep learning models using TensorFlow 2.0.
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Hands-on Feature Engineering for Natural Language Processing
Susan Li shares various NLP feature engineering techniques, from Bag-Of-Words to TF-IDF to word embedding that includes feature engineering for both ML models and an emerging deep learning approach.
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Deep Learning for Recommender Systems
Oliver Gindele discusses how some DL models can be implemented in TensorFlow, starting from a collaborative filtering approach and extending that to more complex deep recommender systems.
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Panel: First Steps with Machine Learning
The panelists discuss the first principles to follow when adding ML to a system.
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Deep Learning with Audio Signals: Prepare, Process, Design, Expect
Keunwoo Choi introduces what the audio/music research societies have discovered while playing with deep learning when it comes to audio classification and regression.
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DeepRacer and DeepLens, Machine Learning for Fun! (and Profit?)
Jeremy Edberg shares his work with DeepRacer and DeepLens, talking about some of the basics of ML used in these projects and showing a DeepRacer in action.
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Reinforcement Learning: A Gentle Introduction with a Real Application
Christian Hidber shows “how” and “why” Reinforcement Learning works, using as a practical example siphonic roof drainage.
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Policing the Capital Markets with ML
Cliff Click talks about SCORE, a solution for doing Trade Surveillance using H2O, Machine Learning, and a whole lot of domain expertise and data munging.
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On a Deep Journey towards Five Nines
Aashish Sheshadri discusses how PayPal applies Seq2Seq networks to forecasting CPU and memory metrics at scale.
