The TensorFlow team has been on a journey to make the training, deployment, managing, and scaling of machine learning Machine Learning models as easy as possible.

TensorFlow 2.0 provides a comprehensive ecosystem of tools for developers, enterprises, and researchers who want to push the state-of-the-art machine learning and build scalable ML-powered applications.

This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish.

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Coding TensorFlow →

Machine Learning represents a new paradigm in programming, where instead of programming explicit rules in a language such as Java or C++, you build a system which is trained on data to infer the rules itself.

But what exactly does ML actually look like?

In part one of Machine Learning Zero to Hero, AI Advocate Laurence Moroney walks through a basic Hello World example of building an ML model, introducing ideas which we’ll apply in later episodes to a more interesting problem: computer vision.

Try this code out for yourself in the Hello World of Machine Learning:

This talk from io19 is for people who know how to code, but who don’t necessarily know machine learning.

Watch this video to learn the ‘new’ paradigm of machine learning, and how models are an alternative implementation for some logic scenarios, as opposed to writing if/then rules and other code.

TensorFlow’s high-level APIs help you through each stage of your model-building process.

On this episode of TensorFlow Meets, Laurence Moroney talks with TensorFlow Engineering Manager Karmel Allison about how TF 2.0 will make building models much easier.

In this talk from the most recent O’Reilly AI Conference, Laurence Moroney from Google talked about Machine Learning, AI, Deep Learning and more, and how they fit the programmers toolkit. He introduced what it’s all about, cutting through the hype, to show the opportunities that are available in Machine Learning. He also introduced TensorFlow, and how it’s a framework that’s designed to make Machine Learning easy and accessible, and how intelligent apps that use ML can run in a variety of places including mobile, web and IoT.

Dyad X Machina is a research partnership that combines affective neuroscience and deep learning. In this episode, Laurence meets with the co-founder,  Haohan Wang, who explains Dyad’s mission as bringing emotion into machine learning.

Watch to learn more about the intersection of deep learning and affective computing and Haohan’s four P’s of learning.

TensorBoard is a suite of visualization tools that make it easier to understand, debug, and optimize TensorFlow programs.

Developer Advocate Laurence Moroney speaks with Justine Tunney, the dev lead for TensorBoard, about how the debugger plugin gives you an x-ray into your models.