This course will evaluate one of the largest changes from TensorFlow 1.0 to TensorFlow 2.0 – the tf.data module. This simplified and unified interface makes managing data pipelines easier with tf.data.
TensorFlow 2.0 has made it easier to manage data pipelines with tf.data through their simplified and unified interface. In this course, Designing Data Pipelines with TensorFlow 2.0, you’ll learn to leverage the performance improvements from the TensorFlow data module. First, you’ll discover how to load data into TensorFlow. Next, you’ll explore prepping data for model training and feature engineering. Finally, you’ll learn how to leverage the performance optimizations of the data pipeline. When you’re finished with this course, you’ll have the skills and knowledge of building data pipelines needed to have data ready for model training in TensorFlow.
Course Overview [Autogenerated] everyone Money, Miss Chase de Han and welcome to my course, designing data pipelines with Tensorflow 2.0, I'm currently the manager of data Science. It's Oreo, a PhD in economics from the University of Utah. In this course, we're going to cover one of the major improvements in the tensorflow to Bono. Release TF data module. This module has a number of performance. Improvements will make your life much easier if you're building models in Tensorflow. Some of the major topics that will cover include migrating from tensorflow one point no tensorflow 2.0 loading data into the TF data data, set data object prepping Gaeta Feature engineering and, most importantly, optimizing data pipeline performance. By the end of this course, we'll know how to use a T F data module in tensorflow new allies. Many of the performance improvements before beginning this course you chef familiarity with python as well as a basic understanding of tensorflow knowledge of the cares. FBI's helpful but not required. I hope you join me on this journey. Learn how to optimize pipelines designing data pipelines of tensorflow two point, of course, at plural site