InfoQ Homepage Machine Learning Content on InfoQ
About this Podcast
Intended for architects and senior developers.
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Rosaria Silipo on Codeless Deep Learning and Visual Programming
In the podcast, Rosaria Silipo talks about the emerging trends in deep learning, with focus on low code visual programming to help data scientists apply deep learning techniques without having to code the solution from scratch.
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Francesca Lazzeri on Machine Learning for Time Series Forecasting
In the podcast, we speak with Dr. Francesca Lazzeri on machine learning for time series forecasting as the main topic which included automated machine learning and deep learning for time series data forecasting, as well as other emerging trends in machine learning development and operations areas including data science lifecycle.
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AI, ML and Data Engineering InfoQ Trends Report - August 2021
How AI, ML and Data Engineering are evolving in 2021 as seen by the InfoQ editorial team. Topics discussed include deep learning, edge deployment of machine learning algorithms, commercial robot platforms, GPU and CUDA programming, natural language processing and GPT-3, MLOps, and AutoML.
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Phil Winder on the History, Practical Application, and Ethics of Reinforcement Learning
In this podcast Dr Phil Winder, CEO of Winder Research, sits down with InfoQ podcast co-host Charles Humble. They discuss: the history of Reinforcement Learning (RL); the application of RL in fields such as robotics and content discovery; scaling RL models and running them in production; and ethical considerations for RL.
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About this Podcast
Discussions on intentional corporate culture and software management approaches.
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Dan Kreigh on Building SpaceShipOne and Designing Flying Cars
In this podcast Shane Hastie, Lead Editor for Culture & Methods, spoke to Dan Kreigh about his experiences as the lead structural analyst working on SpaceShipOne and his personal interest in designing and building a flying car.
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CONTENT IN THIS BOX PROVIDED BY OUR SPONSOR
Featured Content
[Neo4j ebook] Knowledge Graphs: Data in Context for Responsive Businesses
Drive intelligent business decisions through the deep context derived from a knowledge graph. Download now.
[Neo4j ebook] Graph Algorithms: Practical Examples in Apache Spark and Neo4j
Whether you are building dynamic network models or forecasting real-world behavior, this book illustrates how graph algorithms deliver value. Download now.
[Neo4j ebook] Fullstack GraphQL Applications with GRANDstack – Essential Excerpts
GRANDstack simplifies the development process by combining a variety of connected data technologies including GraphQL and Neo4j. Get the Book Excerpt.
