TensorFlow.js contributor Kai Sasaki on how TensorFlow.js eases web-based machine learning application development

Running Machine Learning applications on the web browser is one of the hottest trends in software development right now. Many notable machine learning projects are being built with Tensorflow.js. It is one of the most popular frameworks for building performant machine learning applications that run smoothly in a web browser.

Recently, we spoke with Kai Sasaki, who is one of the initial contributors to TensorFlow.js. He talked about current and future versions of TF.js, how it compares to other browser-based ML tools and his contributions to the community. He also shared his views on why he thinks Javascript good for Machine Learning.

If you are a web developer with working knowledge of Javascript who wants to learn how to integrate machine learning techniques with web-based applications, we recommend you to read the book, Hands-on Machine Learning with TensorFlow.js. This hands-on course covers important aspects of machine learning with TensorFlow.js using practical examples. Throughout the course, you'll learn how different algorithms work and follow step-by-step instructions to implement them through various examples.

On how TensorFlow.js has improved web-based machine learning

How do you think Machine Learning for the Web has evolved in the last 2-3 years? What are some current applications of web-based machine learning and TensorFlow.js? What can we expect in future releases?

Machine Learning on the web platform is a field attracting more developers and machine learning practitioners. There are two reasons. First, the web platform is universally available. The web browser mostly provides us a way to access the underlying resource transparently. The second reason is security.raining a model on the client-side means you can keep sensitive data inside the client environment as the entire training process is completed on the client-side itself. The data is not sent to the cloud, making it more secure and less susceptible to vulnerabilities or hacking.

In future releases as well, TensorFlow.js is expected to provide more secure and accessible functionalities. You can find various kinds of TensorFlow.js based applications here.

How does TensorFlow.js compare with other web and browser-based machine learning tools? Does it make web-based machine learning application development easier?

The most significant advantage of TensorFlow.js is the full compatibility of the TensorFlow ecosystem. Not only can a TensorFlow model be seamlessly used in TensorFlow.js, tools for visualization and model deployment in the TensorFlow ecosystem can also be used in TensorFlow.js.

TensorFlow 2 was released in October. What are some new changes made specific to TensorFlow.js as a part of TF 2.0 that machine learning developers will find useful? What are your first impressions of this new release?

Although there is nothing special related to TensorFlow 2.0, the full support of new backends is actively developed, such as WASM and WebGPU. These hardware acceleration mechanisms provided by the web platform can enhance performance for any TensorFlow.js application. It surely makes the potential of TensorFlow.js stronger and possible use cases broader.

On Kai’s experience working on his book, Hands-on Machine Learning with TensorFlow.js

Tell us the motivation behind writing your book Hands-on Machine Learning with TensorFlow.js. What are some of your favorite chapters/projects from the book?

TensorFlow.js does not have much history because only three years have passed since its initial publication. Due to the lack of resources to learn TensorFlow.js usage, I was motivated to write a book illustrating how to using TensorFlow.js practically. I think chapters 4 - 9 of my book Hands-On Machine Learning with TensorFlow.js provide readers good material to practice how to write the ML application with TensorFlow.js.

Why Javascript for Machine Learning

Why do you think Javascript is good for Machine Learning? What are some of the good machine learning packages available in Javascript? How does it compare to other languages like Python, R, Matlab, especially in terms of performance?

JavaScript is a primary programming language in the web platform so it can work as a bridge between the web and machine learning applications. We have several other libraries working similarly. For example, machinelearn.js is a general machine learning framework running with JavaScript. Although JavaScript is not a highly performant language, its universal availability in the web platform is attractive to developers as they can build their machine learning applications that are “write once, run anywhere”. We can compare the performance by running state-of-the-art machine learning models such as MobileNet or ResNet practically.

On his contribution towards TF.js

You are a contributor for TensorFlow.js and were awarded by the Google Open Source Peer Bonus Program. What were your main contributions? How was your experience working for TF.js?

One of the significant contributions I have made was fast Fourier transformation operations. I have created the initial implementation of fft, ifft, rfft and irfft. I also added stft (short term Fourier transformation). These operators are mainly used for performing signal analysis for audio applications. I have done several bug fixes and test enhancements in TensorFlow.js too.

What are the biggest challenges today in the field of Machine Learning and AI in web development? What do you see as some of the greatest technology disruptors in the next 5 years?

While many developers are writing Python programming languages in the machine learning field, not many web developers have familiarity and knowledge of machine learning in spite of the substantial advantage of the integration between machine learning and web platform. I believe machine learning technologies will be democratized among web developers so that a vast amount of creativity is flourished in the next five years. By cooperating with these enthusiastic developers in the community, I believe the machine learning on the client-side or edge device will be one of the major contributions in the machine learning field.

About the author

Kai Sasaki works as a software engineer in Treasure Data to build large-scale distributed systems. He is one of the initial contributors to TensorFlow.js and contributes to developing operators for newer machine learning models. He has also received the Google Open Source Peer Bonus in 2018. You can find him on Twitter, Linkedin, and GitHub.

About the book

Hands-On Machine Learning with TensorFlow.js is a comprehensive guide that will help you easily get started with machine learning algorithms and techniques using TensorFlow.js. Throughout the course, you'll learn how different algorithms work and follow step-by-step instructions to implement them through various examples. By the end of this book, you will be able to create and optimize your own web-based machine learning applications using practical examples.

Baidu adds Paddle Lite 2.0, new development kits, EasyDL Pro, and other upgrades to its PaddlePaddle platform.

Introducing Spleeter, a Tensorflow based python library that extracts voice and sound from any music track

TensorFlow 2.0 released with tighter Keras integration, eager execution enabled by default, and more!