3rd Edition Released — Officially supports Keras and TensorFlow 2.0. Grab your copy now!

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

François CholletCreator of Keras

Take a sneak peek at what's inside...

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

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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.

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 GeitgeyAuthor of Machine Learning is Fun! blog series

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 ZdziarskiPhD in CV and ML, author at Zbigatron

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.

Regardless of your experience level, you'll find tremendous value inside Deep Learning for Computer Vision with Python, I guarantee it.

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.

I'm ready to order my copy now

Utilize Python, Keras, TensorFlow 2.0, and mxnet to build deep learning networks.

Python, TensorFlow 2.0, Keras, and mxnet are all well-built tools that, when combined, create a powerful deep learning development environment that you can use to master deep learning for computer vision and visual recognition.

We'll be utilizing the Python programming language for all examples in this book. Python is an easy language to learn and is hands-down the best way to work with deep learning algorithms.

To build and train our deep learning networks we'll primarily be using TensorFlow 2.0 and the Keras API inside of TF 2.0 (i.e., tf.keras). Using Keras and TensorFlow 2.0 is the fastest, easiest way to go from idea, to experimentation, to result.

We'll also use mxnet, a deep learning library that specializes in distributed, multi-machine learning. The ability to parallelize training across GPUs/devices is critical when training deep neural network architectures on massive datasets (such as ImageNet).

Each library that we use in this book will be thoroughly reviewed to ensure you understand how to build & train your own deep learning networks.

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 BoulangerResearch Assistant in Data Analytics

You're probably wondering...
“Is this book right for me?”

This book is for developers, researchers, and students who have at least some programming experience and want to become proficient in deep learning for computer vision & visual recognition.

If any of these descriptions fit you, rest assured: you're the target student. I wrote this book for you.

  • You are a computer vision developer that utilizes OpenCV (among other image processing libraries) and are eager to level-up your skills.
  • You have experience with machine learning and want to break into neural networks/deep learning for image understanding.
  • You are a college student and want more than your university offers (or want to get ahead of your class).
  • You are a scientist looking to apply deep learning + computer vision algorithms to your research.
  • Your utilize computer vision algorithms in your own projects but have yet to try deep learning.
  • You used deep learning in projects before, but never in the context of visual recognition and image understanding.
  • You write Python/machine learning code at your day job and are motivated to stand out from your coworkers.
  • You are a "machine learning hobbyist" who knows how to program and wants to understand what this "deep learning" thing is all about.

Download the FREE Deep Learning
for Computer Vision Chapter!

See for yourself how my book can help you master deep learning and computer vision.

Adrian possesses a very rare talent of making complex concepts easy to grasp.”

Jean-Francois ParentPython Programmer at ChallengeU

I'm constantly recommending your [PyImageSearch.com] site to people I know at Georgia Tech and Udacity. While I consider Udacity the gold standard, I would rate your material at the same level. Keep up the good work.”

Andrew BakerSenior Software Engineer at Honeywell

Trusted by members of top machine learning companies and schools. Join them in deep learning mastery.

ImageNet Bundle
$595 The most complete deep learning + computer vision education online today

No Risk 100% Money Back Guarantee!

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The ImageNet Bundle is the most in-depth bundle and is for readers who want to train large-scale deep neural networks.

This bundle is also the only bundle that includes a hardcopy edition of the complete Deep Learning for Computer Vision with Python book mailed to your doorstep.

Read More...

Inside this bundle, I demonstrate how to construct an entire Python framework to train network architectures such as AlexNet, VGGNet, SqueezeNet, GoogLeNet, and ResNet from scratch on the challenging ImageNet dataset.

You'll also learn how to train object detection (Faster R-CNNs, Single Shot Detectors, RetinaNet) and image segmentation (Mask R-CNN) networks on your own custom datasets.

Using the training techniques I outline in this bundle, you'll be able to reproduce the results you see in popular deep learning papers and publications — this is an absolute must for anyone doing research and development in the deep learning space.

When it comes to studying deep learning, you can't beat this bundle!

  • All chapters inside the Starter Bundle, Practitioner Bundle and ImageNet Bundle in PDF, EPUB, and MOBI format.
  • BONUS GUIDES on training object detection (Faster R-CNN, Single Shot Detector, RetinaNet) and image segmentation (Mask R-CNN) networks on your own custom datasets.
  • An exclusive hardcopy edition of Deep Learning for Computer Vision with Python mailed right to your doorstep (this is the only bundle that includes a physical copy of the book).
  • Video tutorials and walkthroughs for each chapter.
  • All source code listings so you can run the examples in the book out-of-the-box.
  • A downloadable pre-configured Ubuntu VirtualBox virtual machine that ships with all necessary Python + deep learning libraries you will need to be successful pre-installed.
  • Access to the Deep Learning for Computer Vision with Python companion website.
  • FREE updates as the book is revised and updated.

No Risk 100% Money Back Guarantee!

Order My Copy Now or click here to pay with PayPal

I can't recommend this book enough for anyone who has some basic knowledge of Python and is interested in Deep Learning and computer vision. Looking for an entry point to Deep Learning for image classification? Choose the Starter Bundle. Want to experiment with different well known architectures such as ResNet and GoogleNet? Go for the Practitioner Bundle. Want to train your networks on ImageNet? Get the ImageNet Bundle. This compendium has been an invaluable resource for my ML work.”

Simon WalshConsultant Radiologist, Kings College Hospital

I've learned a lot from the DL4CV book. I purchased an ImageNet bundle. I've learnt a lot from the experiences you [Adrian] put in the book. Especially, I'm mind blown by the way you explained ResNet in Practitioner Bundle and Image Orientation correction from ImageNet bundle.”

Saideep TalariComputer Vision Expert, Security Analyst at SenseHawk

Enjoy a 100% money back guarantee.

After taking this curriculum, if you haven't learned any of the aforementioned courses, then we don't want your money. That's why we offer a 100% Money-Back Guarantee. Simply send us an email and ask for a refund- up to 30 days after your purchase. With all the copies we've sold, we can count the number of refunds on the one hand. Our readers are satisfied, and we're sure you will be too. For subscription products, please cancel before your renewal date. You can cancel at any time, so refunds will not be processed for renewals. Reach out to our team if you are considering canceling, as we'll be happy to generate a custom learning path or point you in the best direction for your current learning. For our complete Terms of Use, please visit: pyimagesearch.com/terms-of-use/

Questions?

Which bundle should I buy?

Each bundle builds on top of the others and includes all content from lower volumes. You should choose a bundle based on (1) how in depth you want to study deep learning, computer vision & visual recognition and (2) your particular budget. Use the "Here's the full breakdown of what you'll learn inside Deep Learning for Computer Vision with Python" section above to help you decide which topics you want to learn, then pick a bundle based on your choices.

Your book is more expensive than other online courses and books — why is your book worth the price?

First, it's important to understand that Deep Learning for Computer Vision with Python is the most complete, comprehensive deep learning education online (the ImageNet Bundle is over 900+ pages). Not only does it cover the theory behind deep learning, it also details the implementation as well. You can't find a book this detailed in any other online platform, MOOC, or book.

Secondly, I personally dedicate time daily to answering your questions, providing help, and offering suggestions — no other book or course online gives you this level of access to authors. To be totally honest with you, I've considered raising the price of this book multiple times but haven't (yet).

My book may seem expensive, but the value you are getting is multiple orders of magnitude higher than any other book or course. I encourage you to give my book a try. Once you dig into the content I'm confident you'll agree that the book is well worth the price.

What deep learning libraries and packages are we using?

We use Keras, TensorFlow 2.0, and mxnet in this book. After years in the trenches as a deep learning researcher and practitioner, I can tell you that the combination of Keras and TensorFlow 2.0 is the fastest, easiest way to go from idea, to experimentation, to result.

The mxnet library specializes in distributed learning, making it a great choice for training deep network architectures on massive datasets.

Each library in the book is thoroughly reviewed to ensure you understand how to build & train your own deep learning networks.

What if I'm a beginner at 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. You'll 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.

Do I need any programming experience before reading this book?

This book assumes you have some prior programming experience (e.g. you know what a variable, function, loop, etc. are). You should have more skills than a novice, but certainly not an intermediate or advanced developer. As long as you understand basic programming logic flow you'll be successful in reading (and understanding) the contents of this book.

Do I need any special hardware to run the examples in the Starter Bundle or Practitioner Bundle?

All examples inside the Starter Bundle can be executed on a CPU without a problem. The same is true for most examples in the Practitioner Bundle, although some examples will take longer to run. In either case, a GPU will dramatically speed up the network training process but is not a requirement.

Can I upgrade from a lower tier bundle to a higher one? How does the upgrade process work?

Yes, you can always upgrade your bundle to a higher one. For example, you could purchase the Starter Bundle and then upgrade to the Practitioner Bundle or ImageNet Bundle at a later date.

The cost to upgrade would simply be the price difference between your current bundle and the bundle you wanted to upgrade to (you would not need to "repurchase" the content you already own). To upgrade your bundle just send me an email and I can get you the upgrade link.

What happens after I purchase?

After you purchase your copy of Deep Learning for Computer Vision with Python you will (1) receive an email receipt for your purchase and (2) you will be able to download your books, code, datasets, etc. immediately. If you purchased the ImageNet Bundle, the only bundle to include a hardcopy edition, you will receive a second email to enter your shipping information.

Why are we using the Python programming language?

First of all, Python is awesome. It is an easy language to learn and hands-down the best way to work with deep learning algorithms. The simple, intuitive syntax allows you to focus on learning the basics of deep learning, rather than spending hours fixing crazy compiler errors in other languages.

I want to learn TensorFlow 2.0. Is TensorFlow 2.0 covered?

Yes, TensorFlow 2.0 is covered inside the text. We primarily use TensorFlow 2.0 and the Keras API inside TensorFlow (i.e., tf.keras) when training our deep neural networks. You'll also learn how to use TensorFlow 2.0 specific features such as GradientTape and eager execution.

What if I'm already experienced in deep learning?

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 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 that 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, research, and projects.

Do I need to know OpenCV?

You do not need to know the OpenCV library to be successful when going through this book. We only use OpenCV to facilitate basic image processing operations such as loading an image from disk, displaying it to our screen, and a few other basic operations. That said, a little bit of OpenCV experience goes a long way, so if you're new to OpenCV I highly recommend (1) purchase a copy of Deep Learning for Computer Vision with Python and (2) work through my other book, Practical Python and OpenCV to learn the fundamentals.

Do I need special hardware for the ImageNet Bundle?

If you intend on going with the ImageNet Bundle, you are expected to have a GPU with at least 6GB of memory. The more GPUs you have available, the better. You should also have at least 1TB of free space on your machine. The ImageNet Bundle covers very advanced deep learning techniques on massive datasets, so make sure you make the necessary hardware preparations.

What GPUs do you recommend for the book?

I personally use the NVIDIA Titan X (12GB) on a daily basis for training my own deep learning networks. The Titan X a bit expensive, so NVIDIA has released the GTX 1080 with 8GB of memory for half the cost of the Titan X. The latest addition to the NVIDIA family, the 1080 Ti (11GB), is also highly recommended. Alternatively, I would recommend using Amazon EC2 and their GPU instances (particularly p2.* and g2.*) in the cloud to train your networks if you do not want to purchase physical hardware.

Can I use the cloud for deep learning?

Yes, you can absolutely use cloud services such as Amazon Web Services (AWS) or Microsoft Azure either with or without a GPU to work through the examples in this book. To jumpstart your education, I have released my own personal pre-configured Amazon Machine Instance (AMI) to help you with your studies and projects. Simply launch an EC2 instance using this pre-configured AMI and you'll be ready to train your own deep neural networks in the matter of minutes! If you're a Microsoft Azure user, you can spin up Microsft DSVM instance and be up and running in a few minutes as well.

Are the hardcopy editions shipping?

Yep, the hardcopies are indeed shipping! The ImageNet Bundle is the only bundle that includes a hardcopy edition. After you purchase, you will receive an email with a link to enter your shipping information. Once I have your shipping address I can get your hardcopy edition in the mail, normally within 48 hours.

Where can I learn more about you?

I have authored over 350+ blog posts about computer vision, OpenCV, and deep learning over at PyImageSearch.com. Check out the posts to get a feel for my teaching and writing style (not to mention the quality and depth of the tutorials). I would also highly suggest that you sign up for the (free) Table of Contents and sample chapters I am offering using the form at the bottom-right corner of this page.

I have another question.

If you have any other questions, please send me a message, and I'll get back to you ASAP.

Who's behind this?

Hey, I'm Adrian Rosebrock, a Ph.D and entrepreneur who has spent his entire adult life studying computer vision and machine learning. Over the past 6 years alone I have:

  • Started the PyImageSearch.com blog and published over 350+ tutorials and articles aimed at teaching computer vision, deep learning, and OpenCV.
  • Created PyImageSearch Gurus, an actionable, real-world course on computer vision and OpenCV. This course is the most comprehensive computer vision education online today, covering 13 modules broken out into 168 lessons with over 2,161 pages of content.
  • Authored Deep Learning for Computer Vision with Python, the most in-depth computer vision and deep learning book available today, including super practical walkthroughs, hands-on tutorials (with lots of code), and a no-nonsense teaching style that will help you master computer vision and deep learning.
  • Published Raspberry Pi for Computer Vision, which covers embedded computer vision and deep learning on devices such as the Raspberry Pi, Google Coral, Movidius NCS, and NVIDIA Jetson Nano.
  • Answered over 50,000+ emails and helped 10,000s of developers, researchers, and students just like yourself learn the ropes of computer vision and deep learning.

If studying deep learning and visual recognition sounds interesting to you, I hope you'll consider grabbing a copy of this book. You'll learn a ton about deep learning and computer vision in a practical, hands-on way. And you'll have fun doing it. See you on the other side!