#tensorflow

Discover how to embrace machine learning in JavaScript using TensorFlow.js in the browser and beyond in this speedy talk. Get inspired through a whole bunch of creative prototypes that push the boundaries of what is possible in the modern web browser (things have come a long way) and then take your own first steps with machine learning in minutes. By the end of the talk everyone will understand how to recognize an object of their choice which could then be used in any creative way you can imagine. Familiarity with JavaScript is assumed, but no background in machine learning is required. Come take your first steps with TensorFlow.js!

Have you ever questioned whether Javascript is a viable alternative to Python or R for creating machine learning models? After all, a 2019 survey by Stack Overflow found that Javascript is the language that developers use the most. Given that machine learning models like neural networks require a lot of computational power and that javascript was not intended for high-speed computing, this approach seems unreasonable. But hold on, this not entirely true, as javascript libraries like Onnx.js and Tensorflow.js are here to save the day! I'll be going into further detail on how to create intuitive and innovative machine learning applications with React in this talk.

Skin cancer is a serious problem worldwide but luckily treatment in the early stage can lead to recovery. JavaScript together with a machine learning model can help Medical Doctors increase the accuracy in melanoma detection. During the presentation, we show how to use Tensorflow.js, Keras and React Native to build a solution that can recognize skin moles and detect if they are a melanoma or a benign mole. We also show issues that we have faced during development. As a summary, we present the pros and cons of JavaScript used for machine learning projects.

We live in exciting times. Frameworks like TensorFlowJS allow us to harness the power of AI models in the browser, while others like Three.js allow us to easily create 3D worlds. In this talk we will see how we can combine both, to build a full solar system, in our browser, using nothing but hand gestures!

TensorFlow has built a solid foundation for various machine learning applications, on top of which the Keras ecosystem can really boost the productivity of the developers in building machine learning solutions. Keras has a simple and arbitrarily flexible API for building and training models. However, we still need a lot of manual work to tune the hyperparameters. Fortunately, with Keras Tuner, we can automate the hyperparameter tuning process with minor modifications to the code for building and training the models. To further boost the productivity, we introduce AutoKeras, which fully automates the model building, training, and hyperparameter tuning process. It dramatically reduces the amount of prior knowledge needed of using machine learning for some common tasks. All you need is to define the task and to provide the training data.

Write and deploy machine learning models easily in Nodejs using Tensorflow.js.

Discover how to embrace machine learning in JavaScript using TensorFlow.js in the browser and beyond in this speedy talk. Get inspired through a whole bunch of creative prototypes that push the boundaries of what is possible in the modern web browser (things have come a long way) and then take your own first steps with machine learning in minutes. By the end of the talk everyone will understand how to recognize an object of their choice which could then be used in any creative way you can imagine. Familiarity with JavaScript is assumed, but no background in machine learning is required. Come take your first steps with TensorFlow.js!

How to build a UI that LEARNS? Being able to learn and predict the behavior of users has many powerful applications, one of them is the chance to boost the UI performance prefetching code
&
resources before the user reaches them. In this talk, we describe a high-level implementation of an intelligent prefetcher, using ReactJS and TensorFlow.js. We use neural networks to learn the user's behaviour, and leverages React's lazy-loading API to prefetch components according to predictions. There is a chance for Frontend developers to explore the powerful combination of UI and AI.