Posted by Josh Gordon for the TensorFlow team.
The TensorFlow blog has moved to https://blog.tensorflow.org. Please visit us there.
Thank you to our readers, and to our many guest authors who have created an incredible collection of content representing a diverse body of work. Thanks also to Medium for a great couple of years.
Although we’ve decided to move our blog, articles from the global TensorFlow community — including students, researchers, and companies large and small — will remain an important part of it.
In the future, new articles will be published on https://blog.tensorflow.org. …
Posted by Aakanksha Chowdhery, Software Engineer
Why does “Yo Google” not work with Google Assistant? After all, it’s only one word different from the phrase “Ok Google”. It’s because Google Assistant is listening for two specific words — or Wake Words. Wake Words are critical to the design of low-power machine learning to process data with a computationally inexpensive model to “wake up” the device for full processing. Audio wake words, such as “Ok Google”, are widely used to wake up AI assistant devices before they process speech using more computationally expensive machine learning models.
With the availability of low-power cameras, a popular application includes using a vision sensor with a microcontroller to classify when an image frame contains a person (or any object of interest). We refer to this application as Visual Wake Words because it enables a device to wake up when a human is present, analogous to how audio wake words are used in speech recognition. …
Posted by Daniel Smilkov, Sandeep Gupta, and Vishy Tirumalashetty
Whether you’re en route to TensorFlow World or you’re unable to make it, learn more below about our new demo experience and explore new GitHub code repos. Be sure to follow #MissionToTensorFlowWorld on Twitter to see this experience in action!
Celebrating the 50th anniversary of the Apollo 11 mission, the first manned mission to land on the Moon, Mission to TensorFlow World is TensorFlow’s latest space game experience that leverages the entire product ecosystem: TensorFlow Core, TensorFlow.js, TensorFlow Lite, and TensorFlow Extended (TFX) to control a spacecraft traveling through space. …
A guest post by Sandeep Mistry & Dominic Pajak of the Arduino team
Arduino is on a mission to make Machine Learning simple enough for anyone to use. We’ve been working with the TensorFlow Lite team over the past few months and are excited to show you what we’ve been up to together: bringing TensorFlow Lite Micro to the Arduino Nano 33 BLE Sense. In this article, we’ll show you how to install and run several new TensorFlow Lite Micro examples that are now available in the Arduino Library Manager.
The first tutorial below shows you how to install a neural network on your Arduino board to recognize simple voice commands. …
Earlier this year, we announced TensorFlow 2.0 in alpha at the TensorFlow Dev Summit. Today, we’re delighted to announce that the final release of TensorFlow 2.0 is now available! Learn how to install it here.
TensorFlow 2.0 is driven by the community telling us they want an easy-to-use platform that is both flexible and powerful, and which supports deployment to any platform. TensorFlow 2.0 provides a comprehensive ecosystem of tools for developers, enterprises, and researchers who want to push the state-of-the-art in machine learning and build scalable ML-powered applications.
TensorFlow 2.0 makes development of ML applications much easier. With tight integration of Keras into TensorFlow, eager execution by default, and Pythonic function execution, TensorFlow 2.0 makes the experience of developing applications as familiar as possible for Python developers. For researchers pushing the boundaries of ML, we have invested heavily in TensorFlow’s low-level API: We now export all ops that are used internally, and we provide inheritable interfaces for crucial concepts such as variables and checkpoints. This allows you to build onto the internals of TensorFlow without having to rebuild TensorFlow. …
A Guest Post by Rodolfo Bonnin from the Mercado Libre Applied Machine Learning team
Mercado Libre is the leading marketplace platform in Latin America, reaching millions of users selling and buying tens of millions of different items a day. From a shipping perspective, one of the most important pieces of information for an item is dimensions and weight since they are used for predicting costs and forecasting the occupancy of the fulfillment centers. As a user-driven marketplace, this information is not always available, and we have found that we can optimize our pipeline by predicting it in advance.
Guest post by Keith Chan and Vincent Zhang from OliveX Engineering team
OliveX is a Hong Kong-based company focused on fitness-related software, serving more than 2 million users since we first launched in 2018. Many of our users are elderly and our Baduanjin app helps them practice Baduanjin while minimizing the possibility of injury. To achieve that, we utilize the latest artificial intelligence technology in our app to automatically detect Baduanjin practicing moves and provide corresponding feedback to our users.
Baduanjin is a popular exercise that consists of eight kinds of limb movements and controlled breathing. …
TensorFlow Extended (TFX) is a platform for creating production-ready ML pipelines. TFX was created by Google and provides the backbone of Google’s ML services and applications, and we’ve been open sourcing TFX for everyone who needs to create production ML pipelines.
TFX can be extended and customized in several ways, including developing new components and including them in your pipeline. A TFX pipeline is a series of TFX components, each of which performs a separate task, which are sequenced as a directed acyclic graph (DAG). In this post we’ll present an example to illustrate the process of developing a new TFX component. …
A guest post by Vasily Konovalov
Dialogue systems have recently become a standard in human-machine interaction, with chatbots appearing in almost every industry to simplify the interaction between people and computers. They can be integrated into websites, messaging platforms, and devices. Chatbots are on the rise, and companies are choosing to delegate routine tasks to chatbots rather than humans, thus providing huge labor cost savings. Unlike humans, chatbots are capable of processing multiple user requests at a time and are always available.
However, many companies don’t know where to start when developing a bot to meet their business needs. Historically, chatbots can be divided into two large groups: rule-based and data-driven. The former relies on predefined commands and templates. Each of these commands should be written by a chatbot developer using regular expressions and textual data analysis. …