Deep Learning for Computer Vision with Python

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.

What is this book?

And what does it cover?

Deep Learning for Computer Vision with Python will make you an expert in deep learning for computer vision and visual recognition tasks.

Inside the book we will focus on:

• Neural Networks and Machine Learning
• Convolutional Neural Networks (CNNs)
• Object detection, localization, and segmentation with deep learning
• Training large-scale (ImageNet-level) networks
• Hands on implementations using the Python programming language and the Keras, TensorFlow 2.0, and mxnet deep learning libraries
• After going through Deep Learning for Computer Vision with Python, you’ll be able to solve real-world problems with deep learning.

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.

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.

Table of Contents

1. Introduction 
2. What ls Deep Learning? 
3. image Fundamentals 
4. image Classification Basics 
5. Datasets for Image Classification 
6. Configuring Your Development Environment 
7. Your First Image Classifier 
8. Parameterized Learning 
9. Optimization Methods and Regularization 
10. Neural Network Fundamentals 
11. Convolutional Neural Networks 
12. Training Your First CNN 
13. Saving and Loading Your Models 
14. LeNet: Recognizing Handwritten Digits 
15. MiniVGGNet: Going Deeper with CNNS 
16. Learning Rate Schedulers 
17. Spotting Underfitting and Overfitting 
18. Checkpointing Models 
19. Visualizing Network Architectures 
20. Out-of-the-box CNNs for Classification 
21. Case Study: Breaking Captchas with a CNN 
22. Case Study: Smile Detection 
23. Your Next Steps 

Reviews

This book is a great, in-depth dive into practical deep learning for computer vision.”

François Chollet

I highly recommend grabbing a copy of Deep Learning for Computer Vision with Python. It goes into a lot of detail and has tons of detailed examples. It’s the only book I’ve seen so far that covers both how things work and how to actually use them in the real world to solve difficult problems. Check it out!”

Adam Geitgey

Phenomenal. The concepts on deep learning are so well explained that I will be recommending this book to anybody not just involved in computer vision but AI in general.”

Dr. Zig Zdziarski

Dr. Rosebrock delivers on what he promises! [Inside the book] he focuses on mastering deep learning concepts, lays down the theoretical foundation, develops interesting deep learning and computer vision projects with detailed explanation of Python scripts, and puts to your disposal priceless expertise to get you quickly engaged in the incredible field of deep learning. There is just no other book like this that I know of!”

David Boulanger