PDF Deep Learning for Computer Vision with Python - Dr. Adrian Rosebrock

332 pages

You're interested in deep learning and computer vision...
...but you don't know how to get started. Let me help.
Whether this is the first time you've worked with machine learning and neural networks or you're already a seasoned deep learning practitioner, Deep Learning for Computer Vision with Python is engineered from the ground up to help you reach expert status.

This book has one goal — to help developers, researchers, and students just like yourself become experts in deep learning for image recognition and classification.

Super practical walkthroughs that present solutions to actual, real-world image classification, object detection, and image segmentation problems, challenges, and competitions.

Hands-on tutorials (with lots of code) that not only show you the algorithms behind deep learning for computer vision but their implementations as well.

A no-nonsense teaching style that is guaranteed to cut through all the cruft and help you master deep learning for image understanding and visual recognition.

Just getting started with deep learning? Or already a pro?
No problem, I have you covered either way.


Are you just getting started in deep learning?
Don't worry; you won't get bogged down by tons of theory and complex equations. We'll start off with the basics of machine learning and neural networks. Learn in a fun, practical way with lots of code. You'll be a neural network ninja in no time, and be able to graduate to the more advanced content.


Are you already a seasoned deep learning pro?
This book isn't just for beginners — there's advanced content in here, too. You'll discover how to train your own custom object detectors and instance segmentation networks using deep learning. You'll build a custom framework that can be used to train very deep architectures on the challenging ImageNet dataset from scratch. I'll even show you my personal blueprint which I use to determine which deep learning techniques to apply when confronted with a new problem. Best of all, these solutions and tactics can be directly applied to your current job and research.