#tensorflow

TensorFlow.js enables machine learning in the browser and beyond, with features like face mesh, body segmentation, and pose estimation. It offers JavaScript prototyping and transfer learning capabilities, as well as the ability to recognize custom objects using the Image Project feature. TensorFlow.js can be used with Cloud AutoML for training custom vision models and provides performance benefits in both JavaScript and Python development. It offers interactivity, reach, scale, and performance, and encourages community engagement and collaboration between the JavaScript and machine learning communities.

Welcome to my lightning talk at React Summit 2023 where I discuss integrating machine learning capabilities in React apps using JavaScript libraries like TensorFlow.js and ONNX.js. These libraries allow for better privacy, lower cost, and lower latency by leveraging system hardware. Examples include using TensorFlow.js and CocoaSSIST to classify images and Ermine.ai for live audio transcription. React developers can now integrate machine learning without needing extensive knowledge of Python or other frameworks.

This Talk discusses using JavaScript to combat skin cancer, with a focus on machine learning integration. The speaker has experience in medical imaging and has partnered with dermatoscopy companies to develop hardware. JavaScript libraries like TensorFlow.js and Pandas.js are used for model deployment and data analysis. The Talk also covers building neural networks, analyzing skin cancer using scoring methods and image processing techniques, and extracting asymmetry in skin images using Python and JavaScript.

This Talk explores the use of TypeScript, 3JS, hand recognition, and TensorFlow.js to create 3D experiences on the web. It covers topics such as rendering 3D objects, adding lights and objects, hand tracking, and creating interactive gestures. The speaker demonstrates how to build a cube and a bouncy box, move objects with flick gestures, and create a solar system with stars and planets. The Talk also discusses the possibilities of using hand gestures for web navigation and controlling websites, as well as the performance limits of these technologies.

This Talk introduces the TensorFlow in Keras ecosystem and highlights its features, including tensor manipulations, automatic differentiation, and deployment. It also discusses the workflow and automation of hyperparameter tuning with Keras Tuner and AutoKeras. The Talk emphasizes the simplicity and productivity of using AutoKeras, which supports various tasks and advanced scenarios. It also mentions the challenges beginners face and provides resources for learning. Lastly, it touches on the use of TensorFlow and Keras in the research domain and the customization options in AutoKeras, including time series forecasting.

The Talk introduces TensorFlow.js in Node.js for machine learning, highlighting its open-source nature and easy integration with JavaScript. It emphasizes the benefits of using Node.js, such as the ability to write machine learning models directly in JavaScript, access to the NPM ecosystem, and improved performance. The different packages available for utilizing TensorFlow.js in Node.js, including CPU, GPU, and vanilla packages, are discussed. The importance of setting up Node.js bindings to avoid blocking the main thread is mentioned, along with the availability of APIs like dfNode and TensorBoard.

JavaScript with TensorFlow.js allows for machine learning in various environments, enabling the creation of applications like augmented reality and sentiment analysis. TensorFlow.js offers pre-trained models for object detection, body segmentation, and face landmark detection. It also allows for 3D rendering and the combination of machine learning with WebGL. The integration of WebRTC and WebXR enables teleportation and enhanced communication. TensorFlow.js supports transfer learning through Teachable Machine and Cloud AutoML, and provides flexibility and performance benefits in the browser and Node.js environments.

Today's talk explores intelligent prefetching in React, including code splitting, lazy loading, and prefetching to improve performance. The use of neural networks for sequence prediction and training with actual user behavior is discussed. React context is used to link UI handlers with predictions and prefetching, enabling dynamic content import and improved user experience. The combination of AI and UI development is showcased in this personal project.