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?
How many members can access this course with a coupon?
TensorFlow: Advanced Techniques Specialization Course coupon is limited to the first 1,000 enrollments. Click 'Enroll Now' to secure your spot and dive into this course on Udemy before it reaches its enrollment limits!
