This is the code repository for Hands-On Deep Learning for Computer Vision [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
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 & effective techniques for image classification, detection, segmentation and generation. You will learn to build your own neural network and able to classify images accordingly. You will be taken through popular techniques such as style transfer - to transfer styles between images. 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.
- 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
To fully benefit from the coverage included in this course, you will need:
To fully benefit from the coverage included in this course, you will need:
Python 3 working knowladge
The basics of Machine Learning - the main ideas behind training, validating and testing ML models
Basic shell skills - how to run a simple command from Terminal
This course has the following software requirements:
This course has the following software requirements:
Conda package manager with Python3.7 (https://conda.io/en/master/miniconda.html )
A code editor, author used Atom in the course
This course has been tested on the following system configuration:
OS: macOS High Sierra
Processor: 1,3 GHz Intel Core 5
Memory: 4 GB
Storage: 121 GB