Autoencoders in TensorFlow 2, Product-Oriented Data Science, and East 2022 Keynote Recaps
Open Data Science Conference (ODSC)
The leading applied data science and AI conference, with 4 global events and nearly 180 meetups worldwide every year.
Here's how autoencoders in TensorFlow 2 could, in principle, be used to decompose inflation into global and local components.?
The success of a machine learning project can often come down to the framework it uses, and here we compare two popular ones: TensorFlow and Keras.?
In product-oriented data science teams, the entire AI-enabled system is considered the end product, not just the model.?
Whether it's innovation labs or part of a university system, researchers are shaping the future of AI with cutting-edge research and discoveries. Here's what's happening in the world of data science in Europe's AI research labs.?
Industry, Opinion, and News
In Luis Vargas’s ODSC East 2022 keynote, “The Big Wave of AI at Scale,” he demonstrates the power of big AI and what Microsoft’s research and engineering means for other companies.?
领英推荐
In this ODSC East 2022 keynote recap, Oracle’s Hari Bhaskar and JR Gauthier discuss current understandings of secure ML within the context of current events.?
Highlighting ODSC Events
Check out the ODSC Europe 2022 AI Expo & Demo Hall and hear from innovating AI companies like Superwise, SAS, Cloudera, iMetit, Neo4j, and more.?
Here's a helpful rundown of all of the virtual sessions coming to ODSC Europe 2022 this June 15th-16th.?
This preview blog of an ODSC Europe 2022 session on using AI to power trading in finance highlights some major takeaways that you can learn from.?
Want to share your research, case studies, thought leadership, and expertise with the data science community? Here's how you can speak at ODSC APAC 2022.
Video of the Week:?Adam Gibson on Deploying Optimized Deep Learning Pipelines
Optimizing Deep Learning Pipelines with new tools such as quantization, model distillation, and just including the right math library can be complicated for end-users. This talk gives a survey on the different techniques for optimizing ML pipelines and lays out trade-offs to consider when deploying them.