Highlights from TensorFlow World in Santa Clara, California 2019
Experts explore TensorFlow 2.0's machine learning capabilities as well as the broader tools and applications of TensorFlow.
Experts explore TensorFlow 2.0's machine learning capabilities as well as the broader tools and applications of TensorFlow.
People from across the TensorFlow community came together in Santa Clara, California for TensorFlow World. Below you’ll find links to highlights from the event.
Jeff Dean explains why Google open-sourced TensorFlow and discusses its progress.
Theodore Summe offers a glimpse into how Twitter employs machine learning throughout its product.
Megan Kacholia explains how Google’s latest innovations provide an ecosystem of tools for developers, enterprises, and researchers who want to build scalable ML-powered applications.
Kemal El Moujahid reveals new developments for the TensorFlow community.
Konstantinos Katsiapis and Anusha Ramesh dive into the insights and approach that helped TensorFlow Extended (TFX) reach its current popularity within Alphabet.
Tony Jebara explains how Spotify improved user satisfaction by building components of the TFX ecosystem into its core ML infrastructure.
Mike Liang discusses TensorFlow Hub, a platform where developers can share and discover pretrained models and benefit from transfer learning.
Anna Roth discusses human and technical factors and suggests future directions for training machine learning models.
Jared Duke and Sarah Sirajuddin explore on-device machine learning and the latest updates to TensorFlow Lite.
Ankur Narang discusses sticker recommendations with multilingual support, a key innovation driven by sophisticated natural language processing (NLP) algorithms.
Sandeep Gupta and Joseph Paul Cohen introduce the TensorFlow.js library.
Chris Lattner and Tatiana Shpeisman explain how MLIR addresses the complexity caused by software and hardware fragmentation.