Expand your skill set and master TensorFlow. Customize your machine learning models through four hands-on courses!
TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing.
About this Specialization
Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs.
This Specialization is for software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.
Looking for a place to start? Master foundational basics with the DeepLearning.AI TensorFlow Developer Professional Certificate.
Ready to deploy your models to the world? Learn how to go live with the TensorFlow: Data and Deployment Specialization.
Applied Learning Project
In this Specialization, you will gain practical knowledge of and hands-on training in advanced TensorFlow techniques such as style transfer, object detection, and generative machine learning.
Course 1: Understand the underlying basis of the Functional API and build exotic non-sequential model types, custom loss functions, and layers.
Course 2: Learn how optimization works and how to use GradientTape and Autograph. Optimize training in different environments with multiple processors and chip types.
Course 3: Practice object detection, image segmentation, and visual interpretation of convolutions.
Course 4: Explore generative deep learning and how AIs can create new content, from Style Transfer through Auto Encoding and VAEs to Generative Adversarial Networks.