TensorFlow: Advanced Techniques Specialization

TensorFlow: Advanced Techniques Specialization

What you will learn from the course

  • 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.
  • Compare Functional and Sequential APIs
  • Build custom loss functions
  • Learn about Tensor objects, the fundamental building blocks of TensorFlow
  • How to use a TensorFlow tool to calculate gradients
  • Learn about the benefits of generating code that runs in graph mode, take a peek at what graph code looks like
  • Image classification, image segmentation, object localization, and object detection
  • Implement image segmentation using variations of the fully convolutional network (FCN) including U-Net and d) Mask-RCNN to identify and detect numbers, pets, zombies, and more
  • Identify which parts of an image are being used by your model to make its predictions using class activation maps and saliency maps
  • Learn neural style transfer using transfer learning
  • Build simple AutoEncoders on the familiar MNIST dataset, and more
  • Explore Variational AutoEncoders (VAEs) to generate entirely new data, and generate anime faces to compare them against reference images
  • Learn about GANs; their invention, properties, architecture

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.

How to Enroll TensorFlow: Advanced Techniques Specialization course?

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