Basic fundamentals of TensorFlow
Using Computer Vision for modeling of images
Learn to implement models for Natural Language Processing
Some exciting tips & tricks for designing, training and evaluating the models.
TensorFlow in a production setting
Use TPU's and AutoML for developing smart applications

Google's TensorFlow framework is the current leading software for implementing and experimenting with the algorithms that power AI and machine learning. Google deploys TensorFlow for many of its products, such as Translate and Maps.

We will embark on this journey by quickly wrapping up some important fundamental concepts, followed by a focus on TensorFlow to complete tasks in computer vision and natural language processing. You will be introduced to some important tips and tricks necessary for enhancing the efficiency of our models. We will highlight how TensorFlow is used in an advanced environment and brush through some of the unique concepts at the cutting edge of practical AI.

If you want to develop a solid foundation on using TensorFlow and continue your journey into advancing the state of the art in AI to create your own smart machine learning solutions, this course is for you.

All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/-Learn-Artificial-Intelligence-with-TensorFlow

Style and Approach
This is an introductory course with a right balance between theory and practical implementation.

Features
Introduction to building models for computer vision and natural language processing.
Overview of the many utilities that TensorFlow has to offer, such as TensorBoard, Estimators, and more.
Learn how models are made in production settings, and how to best structure your TensorFlow programs.