Announcing the winners of the #PoweredByTF 2.0 Dev Post Challenge
Posted by the TensorFlow team
At the 2019 TensorFlow Dev Summit we announced the Powered by TF Challenge on DevPost specifically for users to create and share their latest and greatest with TensorFlow 2.0.
Thank you to everyone who joined the challenge, we had over 600 participants. We loved seeing what you built, ranging from an app that shows memes and asserts how you feel and try to cheer you up, to using Convolutional Neural Networks to predict noise in lab-rat MRI signals.
Today we’re excited to announce our winners. We’ll be featuring their work on our blog over the next month, so make sure you check back in to learn more about how they hacked on 2.0.
In the meantime, continue hacking on newly released TensorFlow 2.0 beta and provide feedback! For more on TensorFlow 2.0, join our developers mailing list, file issues with the 2.0 tag, and check out our docs.
Huskarl is a framework for deep reinforcement learning focused on research and fast prototyping. It’s built on TensorFlow 2.0 and uses the tf.keras API when possible for conciseness and readability.
Huskarl makes it easy to parallelize computation of environment dynamics across multiple CPUs. This is useful for speeding up on-policy learning algorithms that benefit from multiple concurrent sources of experience such as A2C or PPO. It is especially useful for computationally intensive environments such as physics-based ones.
2. Nbody.ai: a Python 3 package for generating N-body simulations
A python 3 package for generating N-body simulations, computing transit timing variations (TTV) and retrieving orbit parameters and uncertainties from TTV measurements within a Bayesian framework. Machine learning is used to estimate the orbit parameters and constraints priors before running a retrieval to model orbital perturbations.
3. HandTrack.js: a library for prototyping hand gestures in the browser
Handtrack.js is a library for prototyping real time hand detection (bounding box), directly in the browser. Underneath, it uses a trained convolutional neural network that provides bounding box predictions for the location of hands in an image. The convolutional neural network (ssdlite, mobilenetv2) is trained using the TensorFlow object detection api.
4. DeepPavlov: an open-source library for end-to-end dialog systems and chatbots
The DeepPavlov team built a framework for building dialogue systems that include all state-of-the-art (SOTA) NLP components. The framework contains a set of SOTA NLP models including Named-Entity Recognition (NER), Open-Domain Question Answering (ODQA) and more.
Specifically, their goal is to provide AI-application developers and researchers with a set of pre-trained NLP models, pre-defined dialog system components (ML/DL/Rule-based) and pipeline templates and a framework for implementing and testing their own dialog models
5. Disaster Watch: uses classification on tweets to identify natural disasters
Disaster Watch is a disaster mapping platform that collects data from twitter, extracts disaster-related information from tweets, and visualizes the results on a map. It enables users to quickly locate all the information in different geographic areas at a glance, and to find the physical constraints caused by the disaster, such as non-accessible river bridges, and take an informed action. Such information helps public and disaster responders.
