Advanced Computer Vision with TensorFlow

Advanced Computer Vision with TensorFlow

English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 1h 58m | 540 MB

Exploit the power of TensorFlow to create powerful image processing applications

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.

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
Table of Contents

01 The Course Overview
02 Loading and Exploring CIFAR10 Dataset
03 SqueezeNet Architecture Design
04 SqueezeNet Implementation
05 Training and Evaluating SqueezeNet
06 Loading and Exploring Flower Dataset
07 ResNet Architecture Design
08 ResNet Implementation
09 Training and Evaluating ResNet
10 Loading and Exploring ImageNet Dataset
11 Xception Architecture Design
12 Xception Implementation
13 Training and Evaluating Xception
14 Loading and Exploring MNIST Dataset
15 ACGAN Architecture Design
16 ACGAN Implementation
17 Training and Evaluating ACGAN