5月 25, 2018 —
Posted by Marcus Chang, Program Manager
Over 7000 people attended I/O this year! TensorFlow was well represented with 7 talks and the AI & Machine Learning sandbox for attendees to explore what’s new!
Sessions featured (you can view the entire playlist here)…
TensorFlow for JavaScript
TensorFlow has been extended to simplify model training and deployment using the JavaScript language. Watch thi…
On the forefront of deep learning research is a technique called reinforcement learning, which bridges the gap between academic deep learning problems and ways in which learning occurs in nature in weakly supervised environments. This technique is heavily used when researching areas like learning how to walk, chase prey, navigate complex environments, and even play Go. This session teaches a neural network to play the video game Pong from just the pixels on the screen. No rules, no strategy coaching, and no PhD required.
Distributed TensorFlow training
To train Machine Learning models effectively, you need to distribute training jobs to multiple machines in a cluster. TensorFlow offers rich functionality to achieve this. Watch this recap to learn how to set this up.
The AI & Machine Learning sandbox introduced attendees to cool demos built with TensorFlow:
TensorFlow
Our TensorFlow team members were on hand to answer questions and show code! To get started with TensorFlow, go to www.tensorflow.org.
TensorFlow Lite
A lightweight machine learning library and tools for mobile and embedded devices. To get started with TensorFlow Lite, go to: tensorflow.org/mobile/tflite. Code can be found here: github.com/tensorflow/tensorflow
Magenta
A research project exploring the role of machine learning in the process of creating art and music. It’s an exploration in building smart tools and interfaces that allow creative coders, artists and musicians to extend (not replace!) their processes using these models.
Develop your own interface for creating music with TensorFlow.js and Magenta.js @ magenta.tensorflow.org/js. Explore Latent Spaces for melodies and rhythms with MusicVAE, a machine-learning powered generative tool that enables controllable and expressive variations through Latent Loops.
Donkey Car @ DIYRobocars
An open source DIY self driving platform for small scale cars. If you’re curious about these miniature self-driving cars, you can build your own @ donkeycar.com and race them at diyrobocars.com!
Other machine learning powered demos included:
Storyboard
This Android app uses machine learning to transform your videos into single-page comic layouts! In Storyboard, a machine learning algorithm selects video frames, which are then mapped to panels in a comic layout. Each panel is then cropped, zoomed, and stylized using research on machine perception from Google AI. And it all runs entirely on your device!
Try it out on Google Play!
Semantris
Word association games powered by machine learning. Built with Universal Sentence Encoder, the module encodes text into high dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks. You can play the games here and learn more about experiments in text understanding here.
Learn with Google AI
Machine Learning Crash Course (MLCC) with TensorFlow APIs, Google’s fast-paced practical introduction to machine learning. MLCC features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises. Free, online, and self-paced! Start your training now @ g.co/mledu/mlcc-io
5月 25, 2018
—
Posted by Marcus Chang, Program Manager
Over 7000 people attended I/O this year! TensorFlow was well represented with 7 talks and the AI & Machine Learning sandbox for attendees to explore what’s new!
Sessions featured (you can view the entire playlist here)…
TensorFlow for JavaScript
TensorFlow has been extended to simplify model training and deployment using the JavaScript language. Watch thi…