by Jakub Konczyk | Released January 2019 | ISBN: 9781788835503

Generate, detect, and classify images faster and more accuratelyAbout This VideoLearn Deep Learning techniques commonly used for Computer Vision: from denoising to classification/similarity matching, image generation, and object detectionExplore tools such as Keras, TensorFlow, and OpenCV to build computer vision applicationsHands-on training in using Deep Neural Networks in Computer Vision applications to build intelligent image-processing modelsIn DetailMachine Learning, and Deep learning techniques in particular, are changing the way computers see and interact with the World. From augmented and mixed-reality applications to just gathering data, these new techniques are revolutionizing a lot of industries This course is designed to give you a hands-on learning experience by going from the basic concepts to the most current in-depth Deep Learning methods for Computer Vision in use today.In this course, you will be introduced to the concept of deep learning and a variety of popular and effective techniques for image classification, detection, segmentation and generation. You will learn to build your own neural network and classify images accordingly. You will be taken through popular techniques such as Deep Dream (to generate psychedelic, surreal images), Style Transfer (to transfer styles between images), and Neural Doodle, to generate an image that matches a doodled sketch.By the end of this course, you will be able to use computer vision and deep learning to encode, classify, detect, and style images for the real world.The code bundle for this video course is available at - https://github.com/PacktPublishing/Hands-On-Deep-Learning-for-Computer-VisionDownloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you. Show and hide more

1. Chapter 1 : DL Overview and Denoising Images with Autoencoders
   • The Course Overview 00:03:50
   • A High-Level Overview of Deep Learning 00:08:38
   • Installing Keras and TensorFlow 00:06:20
   • Building a CNN Based Autoencoder to Denoise Images 00:22:07
   • Summary 00:01:09
2. Chapter 2 : Image Classification with Keras
   • An Introduction to ImageNet Dataset and VGG Model 00:06:13
   • Using a Pre-Trained VGG Model 00:11:32
   • Summary and What’s Next? 00:01:56
3. Chapter 3 : Construct a GAN with Keras
   • Introduction to GANs 00:03:45
   • Building GANs to Learn MNIST Dataset 00:19:47
   • Summary and What’s Next? 00:01:22
4. Chapter 4 : Object Detection with YOLO
   • An Introduction to Object Detection and YOLO 00:06:01
   • Installing and Setting Up Keras Implementation of YOLO 00:09:37
   • Using a Pre-Trained YOLO Model for Object Detection 00:06:47
   • Summary and What’s Next? 00:02:55
5. Chapter 5 : Generating Images with Neural Style
   • An Introduction to Neural Style Transfer 00:05:52
   • Using Keras Implementation of Neural Style Transfer 00:04:58
   • Summary 00:01:07
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