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Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brand-new version of Google's open source framework for machine learning

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Last Updated

March 4, 2021

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This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course.In this course you learn about:

· Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras

· Apply modern solutions to a wide range of applications such as object detection and video analysis

· Run your models on mobile devices and web pages and improve their performance.

· Create your own neural networks from scratch

· Classify images with modern architectures including Inception and ResNet

· Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net

· Tackle problems faced when developing self-driving cars and facial emotion recognition systems

· Boost your application’s performance with transfer learning, GANs, and domain adaptation

· Use recurrent neural networks (RNNs) for video analysis

· Optimize and deploy your networks on mobile devices and in the browser

Course Curriculum

    • Computer Vision and Neural Networks 00:00:00
    • Technical requirements 00:00:00
    • Computer vision in the wild 00:00:00
    • A brief history of computer vision 00:00:00
    • Getting started with neural networks 00:00:00
    • TensorFlow Basics and Training a Model 00:00:00
    • Technical requirements 00:00:00
    • Getting started with TensorFlow 2 and Keras 00:00:00
    • TensorFlow 2 and Keras in detail 00:00:00
    • The TensorFlow ecosystem 00:00:00
    • Modern Neural Networks 00:00:00
    • Technical requirements 00:00:00
    • Discovering convolutional neural networks 00:00:00
    • Refining the training process 00:00:00
    • Influential Classification Tools 00:00:00
    • Technical requirements 00:00:00
    • Understanding advanced CNN architectures 00:00:00
    • Leveraging transfer learning 00:00:00
    • Object Detection Models 00:00:00
    • Technical requirements 00:00:00
    • Introducing object detection 00:00:00
    • A fast object detection algorithm – YOLO 00:00:00
    • Faster R-CNN – a powerful object detection model 00:00:00
    • Enhancing and Segmenting Images 00:00:00
    • Technical requirements 00:00:00
    • Transforming images with encoders-decoders 00:00:00
    • Understanding semantic segmentation 00:00:00
    • Training on Complex and Scarce Datasets 00:00:00
    • Technical requirements 00:00:00
    • Efficient data serving 00:00:00
    • How to deal with data scarcity 00:00:00
    • Video and Recurrent Neural Networks 00:00:00
    • Technical requirements 00:00:00
    • Introducing RNNs 00:00:00
    • Classifying videos 00:00:00
    • Optimizing Models and Deploying on Mobile Devices 00:00:00
    • Technical requirements 00:00:00
    • Optimizing computational and disk footprints 00:00:00
    • On-device machine learning 00:00:00
    • Example app – recognizing facial expressions 00:00:00

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