Hands-On Deep Learning for Computer Vision

Hands-On Deep Learning for Computer Vision
Hands-On Deep Learning for Computer Vision
English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 04m | 419 MB

Go from auto encoding to cutting-edge imaging techniques such as YOLO and Neural Doodle with Keras, TensorFlow, OpenCV, and Python

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.

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

What You Will 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
Table of Contents

DL Overview and Denoising Images with Autoencoders
1 The Course Overview
2 A High-Level Overview of Deep Learning
3 Installing Keras and TensorFlow
4 Building a CNN Based Autoencoder to Denoise Images
5 Summary

Image Classification with Keras
6 An Introduction to ImageNet Dataset and VGG Model
7 Using a Pre-Trained VGG Model
8 Summary and What’s Next

Construct a GAN with Keras
9 Introduction to GANs
10 Building GANs to Learn MNIST Dataset
11 Summary and What’s Next

Object Detection with YOLO
12 An Introduction to Object Detection and YOLO
13 Installing and Setting Up Keras Implementation of YOLO
14 Using a Pre-Trained YOLO Model for Object Detection
15 Summary and What’s Next

Generating Images with Neural Style
16 An Introduction to Neural Style Transfer
17 Using Keras Implementation of Neural Style Transfer
18 Summary