Advanced Computer Vision with TensorFlow [Video]

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Advanced Computer Vision with TensorFlow [Video]

Marvin Bertin

Exploit the power of TensorFlow to perform image processing

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Video Details

ISBN 139781788479448
Course Length1 hour 58 minutes

Video Description

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.

Table of Contents

SqueezeNet: Efficient CNN Architecture
The Course Overview
Loading and Exploring CIFAR10 Dataset
SqueezeNet Architecture Design
SqueezeNet Implementation
ResNet: Residual Learning for Image Recognition
Loading and Exploring Flower Dataset
ResNet Architecture Design
ResNet Implementation
Training and Evaluating ResNet
Xception: Depthwise Separable Convolutions
Loading and Exploring ImageNet Dataset
Xception Implementation
Training and Evaluating Xception
Xception Architecture Design
Conditional Generative Adversarial Network
Loading and Exploring MNIST Dataset
ACGAN Architecture Design
ACGAN Implementation
Training and Evaluating ACGAN

What You Will Learn

  • Build efficient architecture for convolutional neural networks
  • Construct a residual learning neural network for image recognition
  • Build depthwise separable convolutional neural networks
  • Construct conditional Generative Adversarial Networks (GAN)
  • Build an advanced and powerful multi-class image classifier
  • Build functional model class and methods with TensorFlow-Keras’ Functional API
  • Build a computational graph representation of a Neural Network from state-of-the-art deep learning papers
  • Optimize a neural network with stochastic gradient descent and other advanced optimization methods

Authors

Table of Contents

SqueezeNet: Efficient CNN Architecture
The Course Overview
Loading and Exploring CIFAR10 Dataset
SqueezeNet Architecture Design
SqueezeNet Implementation
ResNet: Residual Learning for Image Recognition
Loading and Exploring Flower Dataset
ResNet Architecture Design
ResNet Implementation
Training and Evaluating ResNet
Xception: Depthwise Separable Convolutions
Loading and Exploring ImageNet Dataset
Xception Implementation
Training and Evaluating Xception
Xception Architecture Design
Conditional Generative Adversarial Network
Loading and Exploring MNIST Dataset
ACGAN Architecture Design
ACGAN Implementation
Training and Evaluating ACGAN

Video Details

ISBN 139781788479448
Course Length1 hour 58 minutes
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