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Laurence Moroney and Eddy Shyu

About TensorFlow

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About TensorFlow

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.

About you

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.

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What's inside

Four courses

Custom Models, Layers, and Loss Functions with TensorFlow

(0 hours)
In this course, you will learn to: • Compare Functional and Sequential APIs, discover new models with the Functional API, and build a model with multiple outputs. • Build custom loss functions to measure model performance. • Build custom layers for your models and customize a network layer with a lambda layer. • Build custom models by defining your own class and build a residual network (ResNet).

Custom and Distributed Training with TensorFlow

(0 hours)
In this course, you will learn about Tensor objects, the fundamental building blocks of TensorFlow, and understand the difference between the eager and graph modes in TensorFlow.

Advanced Computer Vision with TensorFlow

(0 hours)
In this course, you will explore image classification, image segmentation, object localization, and object detection. Apply transfer learning to object localization and detection. Apply object detection models such as regional-CNN and ResNet-50, customize existing models, and build your own models to detect, localize, and label your own images.

Generative Deep Learning with TensorFlow

(0 hours)
In this course, you will learn about neural style transfer, AutoEncoders, Variational AutoEncoders (VAEs), and Generative Adversarial Networks (GANs). You will build models using TensorFlow to generate new images, de-noise noisy images, and generate entirely new data.

Learning objectives

  • Understand the underlying basis of the functional api and build exotic non-sequential model types, custom loss functions, and layers.
  • Learn optimization and how to use gradienttape & autograph, optimize training in different environments with multiple processors and chip types.
  • Practice object detection, image segmentation, and visual interpretation of convolutions.
  • Explore generative deep learning, and how ais can create new content, from style transfer through auto encoding and vaes to gans.

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