Federated learning with TensorFlow Federated

Federated learning with TensorFlow Federated

This TensorFlow tutorial explains the key concepts behind Federated Learning (FL) and TensorFlow Federated (TFF), how to set up a FL experiment and run it in a simulator, what the code looks like and how to extend it. TensorFlow Federated (TFF) is an open-source framework for machine learning and other computations on decentralized data. TFF has been developed to facilitate open research and experimentation with Federated Learning (FL), an approach to machine learning where a shared global model is trained across many participating clients that keep their training data locally.

TensorFlow Federated (TFF) is an open-source framework for machine learning and other computations on decentralized data. TFF has been developed to facilitate open research and experimentation with Federated Learning (FL), an approach to machine learning where a shared global model is trained across many participating clients that keep their training data locally. By eliminating the need to collect data at a central location, yet still enabling each participant to benefit from the collective knowledge of everything in the network, FL lets you build intelligent applications that leverage insights from data that might be too costly, sensitive, or impractical to collect.

In this session, we explain the key concepts behind FL and TFF, how to set up a FL experiment and run it in a simulator, what the code looks like and how to extend it, and we briefly discuss options for future deployment to real devices.

Let's Build A Video Game With Unity, TensorFlow and Python

Let's Build A Video Game With Unity, TensorFlow and Python

In this Machine Learning tutorial, we’ll build a video game with Unity, TensorFlow and Python. We’ll show you how easy it is to add ML-powered intelligence to video games or simulations, and how inference on smartphones is easier than it’s ever been: modern, powerful tools like Unity’s ML-Agents, Python, and TensorFlow make the complex easy. And it’s a lot of fun.

In this session, we’ll build a little smartphone game, train a bot to play it using reinforcement learning, Python, and TensorFlow, and deploy it to a smartphone.

In 30minutes. We’ll show you how easy it is to add ML-powered intelligence to video games or simulations, and how inference on smartphones is easier than it’s ever been: modern, powerful tools like Unity’s ML-Agents, Python, and TensorFlow make the complex easy. And it’s a lot of fun.

First, we’ll spend 10 minutes of the session:

  • showcasing the absolute basics game engines

  • creating an arcade game, live on stage

  • adding some art, to make the game look pretty!

Second, we’ll spend 10 minutes of the session:

  • implementing an agent, using Python and TensorFlow, that is rewarded for playing the game

  • training the agent to play

  • giving the agent some character

Finally, we’ll spend the last 10 minutes of the session:

  • preparing our trained model for deployment onto a smartphone

  • building the game and optimizing both the gameplay and ML-components for a smartphone

  • showing the audience the game, running live on a phone!

This is an engaging, fast-paced, and surprisingly in-depth exploration of how powerful modern game engines can be used for quick, relatively easy, but incredibly powerful state of the art machine learning and training, and how powerful inference on-device is, for mobile AI.

You don’t need to be a game developer to see the benefits. Join us for 30minutes of rapid live coding, and engaging banter while we show what’s really possible with Python, TensorFlow for ML, game engines/simulations, and mobile devices!

In this session, we'll build a little smartphone game, train a bot to play it using reinforcement learning with Python and TensorFlow, and deploy it to a smartphone. In 30 minutes we'll show you how easy it is to add machine learning (ML)-powered intelligence to video games and simulations, and how useful it can be to visualize ML problems. It'll be fun, and you'll learn the fundamentals of ML.

Tensorflow Tutorial for Beginners - Tensorflow on Neural Networks

Tensorflow Tutorial for Beginners - Tensorflow on Neural Networks

In this TensorFlow tutorial for beginners - TensorFlow on Neural Networks, you will learn TensorFlow concepts like what are Tensors, what are the program elements in TensorFlow , what are constants & placeholders in TensorFlow Python, how variable works in placeholder and a demo on MNIST.

TensorFlow Tutorial for Beginners - TensorFlow on Neural Networks

In this TensorFlow tutorial for beginners - TensorFlow on Neural Networks, you will learn TensorFlow concepts like what are Tensors, what are the program elements in Tensorflow, what are constants & placeholders in TensorFlow Python, how variable works in placeholder and a demo on MNIST.

Building Machine Learning models in Python with TensorFlow 2.0

Building Machine Learning models in Python with TensorFlow 2.0

In this TensorFlow 2.0 tutorial, you’ll understanding of how you can get started building machine learning models in Python with TensorFlow 2.0 as well as the other exciting available features!

Learn about the updates being made to TensorFlow in its 2.0 version. We’ll give an overview of what’s available in the new version as well as do a deep dive into an example using its central high-level API, Keras. You’ll walk away with a better understanding of how you can get started building machine learning models in Python with TensorFlow 2.0 as well as the other exciting available features!