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 Part 3 of this mini-series on TensorFlow high-level APIs, TensorFlow Engineering Manager Karmel Allison runs us through different scenarios using TensorFlow’s high-level APIs.

With the TensorFlow model defined, Karmel discusses how to establish layer architecture, and compile the model, adding the optimizer, loss, and metrics that we are interested in. She also runs through the various steps taken to refine the model.

TensorFlow Engineering Manager Karmel Allison walks through different scenarios using TensorFlow’s high-level APIs.

Building a ML model takes a lot of time, effort, and often involves multiple stages. Fortunately, TensorFlow high-level APIs aim to help you along with each stage, from the start of your idea, to training and serving large scale applications. Watch to discover the key steps in developing machine learning models, where TensorFlow comes in for each step, and lastly how to prepare and load your data!