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Hands-On Deep Learning for Computer Vision [Video]

More Information
Learn
  • Hands-on experience using deep learning with Python, Keras, TF, and OpenCV 
  • Encode, decode, and denoise images with autoencoders 
  • Understand the structure and function of neural networks and CNNs/pooling 
  • Classify images with OpenCV using smart Deep Learning methods 
  • Detect objects in images with You Only Look Once (YOLOv3) 
  • Work with advanced imaging tools such as Deep Dream, Style Transfer, and Neural Doodle
About

Machine 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-Vision

Style and Approach

This video course offers a project-based approach to teach you the skills required to develop computer vision applications using Deep Learning and Python.

Features
  • Learn Deep Learning techniques commonly used for Computer Vision: from denoising to classification/similarity matching, image generation, and object detection 
  • Explore tools such as Keras, TensorFlow, and OpenCV to build computer vision applications 
  • Hands-on training in using Deep Neural Networks in Computer Vision applications to build intelligent image-processing models
Course Length 2 hours 4 minutes
ISBN9781788835503
Date Of Publication 31 Jan 2019
Using a Pre-Trained VGG Model
Summary and What’s Next?
Building GANs to Learn MNIST Dataset
Summary and What’s Next?

Authors

Jakub Konczyk

Jakub Konczyk has enjoyed and done programming professionally since 1995. He is a Python and Django expert and has been involved in building complex systems since 2006. He loves to simplify and teach programming subjects and share it with others. He first discovered Machine Learning when he was trying to predict the real estate prices in one of the early stage start-ups he was involved in. He failed miserably. Then he discovered a much more practical way to learn Machine Learning that he would like to share with you in this course. It boils down to “Keep it simple!” mantra. Learn more at https://kubakonczyk.com