TensorFlow has been gaining immense popularity over the past few months, due to its power and simplicity to use. This video will help you leverage the power of TensorFlow to perform advanced image processing. This course is a continuation of the Intro to Computer Vision course, building on top of the skills learned in that course. In this course, you'll dive deeper as we cover more advanced computer vision concepts. .You will implement multiple state-of-the-art deep learning papers from scratch using the TensorFlow-Keras API. This course will teach you how to construct efficient CNN architectures with CNN Squeeze layers and delayed downsampling . You'll learn about residual learning with skip connections and deep residual blocks, and see how to implement a deep residual neural network for image recognition. You'll find out about Google's Inception module and depthwise separable convolutions and understand how to construct an extreme Inception architecture with TF-Keras. Finally, you'll be introduced to the exciting new world of adversarial neural networks, which are responsible for recent breakthroughs in synthetic image generation and implement an auxiliary conditional GAN. Style and Approach. This video course is a practical guide to implementing TensorFlow in production and is packed with step-by-step instructions, working examples, and helpful advice about building your neural networks with TensorFlow, where you'll learn to build separable convolutional neural networks. This practical course is divided into clear byte-size chunks so you can learn at your own pace and focus on the areas that interest you the most.
Target Audience
This course is for Python developers who are interested in learning how to perform image processing using TensorFlow. A basic knowledge of TensorFlow will help you understand the concepts better.
Business Outcomes
- Learn how to build advanced image processing application using free tools and libraries
- Perform advanced image processing with Pythons APIs
- Understand and optimize various features of TensorFlow by building deep learning state-of-the-art models
