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Any blog article, course you make, book you write have become my "shut-up and take my money" kind of deal. I feel I have learned tons already (and I am just starting).

Javier LiendoCV Enthusiast

I did deeplearning.ai, Udacity AI Nanodegree, and a bunch of other courses ... but for the last month, I have always started the day by first finishing one day of your course. The projects are not too overwhelming, but each project gets a key thing done, so they are super useful. I keep on finding myself getting back and looking at the source code from your projects, much more than I do from other courses.

Igor MarjanovicResearcher and Business Owner

Access Our Comprehensive Deep Learning & Computer Vision Library

Here's a sample of the 30 out of 86 courses (and counting) available in PyImageSearch University. Each lesson includes detailed video tutorials, well-documented source code with line-by-line explanations (so you know what the code is doing), Jupyter Notebooks that are pre-configured to run in Google Colab with a single click, and support for all major operating systems (Windows, macOS, Linux, and Raspbian)

PyImageSearch University syllabus

86 Courses (30 shown here) • 115 Classes • 115 Lectures

Loading and Displaying Images with OpenCV (12:15)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Image Fundamentals (15:07)

Drawing with OpenCV (16:38)

Translation (8:20)

Rotation (11:01)

Resizing (13:13)

Flipping (3:04)

Cropping (10:16)

Image Arithmetic (12:14)

Bitwise Operations (7:54)

Masking (5:52)

Splitting and Merging Channels (10:24)

Final exam

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Kernels (24:47)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Morphological Operations (19:53)

Smoothing and Blurring (19:57)

Color Spaces

Basic Thresholding (14:19)

Adaptive Thresholding (16:01)

Image Gradients (19:53)

Edge Detection (14:31)

Automatic Edge Detection (10:46)

Final exam

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Image Histograms (22:55)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Histogram and Adaptive Histogram Equalization (16:10)

Histogram Matching

Gamma Correction (11:26)

Automatic Color Correction (24:13)

Final exam

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Face Detection with Haar Cascades (19:32)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Deep Learning Face Detection with OpenCV (15:42)

Deep Learning Face Detection with Dlib (18:40)

Choosing a Face Detection Method (12:57)

Final exam

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Facial Landmarks with Dlib and and OpenCV (17:36)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Detecting Eyes, Nose, Lips, and Jaw with OpenCV (13:52)

Real-time Facial Landmark Detection (10:41)

5-point Facial Landmark Detection (9:47)

Final exam

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What Is Face Recognition? (11:21)

Lesson Lesson assessment

Face Recognition with Local Binary Patterns (23:29)

OpenCV Eigenfaces for Face Recognition (24:48)

Final exam

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AprilTag Detection (22:43)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Generating ArUco Markers with OpenCV (19:36)

Detecting ArUco Markers with OpenCV (24:08)

Automatically Determining ArUco Marker Type (18:26)

Augmented Reality with ArUco Markers (24:18)

Real-time Augmented Reality with OpenCV (23:11)

Final exam

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What is Deep Learning? (13:34)

Lesson Lesson assessment

Image Classification Basics (6:31)

The Deep Learning Classification Pipeline (5:11)

Your First Image Classifier: Using k-NN to Classify Images

Parameterized Learning and Neural Networks (11:19)

Final exam

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Understanding and Implementing Gradient Descent (27:29)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Stochastic Gradient Descent (SGD) with Python (18:50)

Gradient Descent Algorithms and Variations (16:08)

Regularization Techniques (10:43)

Final exam

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Introduction to Neural Networks (11:02)

Lesson Lesson assessment

Implementing the Perceptron Neural Network with Python (21:21)

Backpropagation from Scratch with Python (39:46)

Implementing Feedforward Neural Networks with Keras and TensorFlow (27:40)

The 4 Key Ingredients When Training Any Neural Network (14:25)

Understanding Weight Initialization for Neural Networks (9:16)

Final exam

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Convolution and Cross-correlation in Neural Networks (15:33)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Convolutional Neural Networks (CNNs) and Layer Types (26:44)

Are CNNs Invariant to Translation, Rotation, and Scaling? (7:11)

Final exam

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A Gentle Guide to Training your First CNN with Keras and TensorFlow (24:26)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Save Your Keras and TensorFlow Model to Disk (9:55)

Load a Trained Keras/TensorFlow Model from Disk (9:16)

LeNet: Recognizing Handwritten Digits

MiniVGGNet: Going Deeper with CNNs (20:54)

Visualizing Network Architectures Using Keras and TensorFlow (7:20)

Pre-trained CNNs for Image Classification (14:58)

Final exam

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Regression with Neural Networks (23:41)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Regression with CNNs (25:15)

Combining Categorical, Numerical, and Image Data Into a Single Neural Network (24:11)

Final exam

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A Gentle Introduction to tf.data with TensorFlow (28:49)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Data Pipelines with tf.data and TensorFlow (22:03)

Data Augmentation with tf.data and TensorFlow

Final exam

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Introduction to Hyperparameter Tuning (24:30)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Hyperparameter Tuning for Computer Vision Projects (16:34)

Using scikit-learn to Tune Deep Learning Model Hyperparameters (18:28)

Easy Hyperparameter Tuning with Keras Tuner (19:52)

Final exam

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What is PyTorch? (24:57)

Lesson Lesson assessment

Your First Neural Network with PyTorch (16:21)

Training Your First CNN with PyTorch (25:45)

Image Classification with Pre-Trained Networks and PyTorch (10:15)

Object Detection with Pre-Trained Networks and PyTorch (11:27)

Final exam

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DataLoader for Image Data (23:07)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

PyTorch: Transfer Learning and Image Classification (47:49)

Introduction to Distributed Training in PyTorch (6:28)

Final exam

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Training a DCGAN in PyTorch (5:54)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Training an object detector from scratch in PyTorch (6:06)

Final exam

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Autoencoders with Keras and TensorFlow (27:23)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Denoising Autoencoders with Keras and TensorFlow (14:16)

Anomaly Detection with Autoencoders (29:04)

Autoencoders for Content-based Image Retrieval (CBIR) (25:30)

Final exam

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Building Image Pairs for Siamese Networks (26:42)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Implementing Your First Siamese Network with Keras and TensorFlow (32:24)

Comparing Images for Similarity with Siamese Networks (23:12)

Improving Accuracy with Contrastlive Loss (30:05)

Final exam

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Adversarial Images and Attacks with Keras and TensorFlow (26:38)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Targeted Adversarial Attacks with Keras and TensorFlow (40:02)

Adversarial Attacks with FGSM (Fast Gradient Signed Method) (21:03)

Defending Against Adverserial Attacks (27:50)

Mixing Normal Images and Adversarial Images when Training CNNs (31:19)

Final exam

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Shape Detection with OpenCV (14:07)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Template Matching with OpenCV (14:52)

Multi-template Matching (15:17)

Multi-scale Template Matching (21:34)

Haar Cascades with OpenCV (13:03)

Deep Learning Object Detectors with OpenCV (17:21)

Real-time Deep Learning Object Detection with OpenCV (15:02)

Final exam

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Turning Any Deep Learning Image Classifier into an Object Detector (44:28)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Selective Search for Object Detection (19:51)

Region Proposal Object Detection (25:34)

Training Your Own R-CNN Object Detector (59:09)

Final exam

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What is Optical Character Recognition (OCR)? (17:20)

Lesson Lesson assessment

Installing Tesseract, PyTesseract, and Python OCR Packages On Your System (5:51)

Your First OCR Project with Tesseract and Python (8:56)

Final exam

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Detecting and OCR’ing Digits with Tesseract and Python (4:15)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Whitelisting and Blacklisting Characters with Tesseract and Python (7:46)

Correcting Text Orientation with Tesseract and Python (7:27)

Language Translation and OCR with Tesseract and Python (7:37)

Using Tesseract with Non-English Languages (15:06)

Final exam

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Making OCR "Easy" with EasyOCR (11:56)

Lesson Code download Pre-configured Jupyter Notebook Lesson assessment

Image/Document Alignment and Registration (19:22)

OCR’ing a Document, Form, or Invoice (25:13)

Final exam

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Welcome to Visual Sensor Fusion (2:13)

Lesson assessment

What to Expect from This Course

Understanding Cameras (11:47)

Understanding LiDARs (8:23)

Review of Sensors in Self-Driving Cars

Sensor Fusion (8:51)

Point Pixel Project (4:08)

Projecting a LiDAR Point (3D) to an Image (2D) (4:30)

Applying the Magic Formula (5:53)

3D-2D Visualizations (11:58)

Coding the Magic Forumula (24:33)

Final exam

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Sign up now to learn everything you need to master Computer Vision, Deep Learning, and OpenCV in simple, intuitive lessons.

What Can You Accomplish with PyImageSearch?

PyImageSearch grads have gone on to accomplish big things with their newfound knowledge.

Will you join them?

  • Dr. Paul Lee used what he learned to perform novel research and publish a paper in the prestigious American Heart Association journal.
  • David Austin, engineer at Intel, took home 1st place (and $25,000) in Kaggle’s most competitive image classification competition of all time using techniques he learned from PyImageSearch
  • Brandon Gilles used the knowledge he learned from PyImageSearch to build the OpenCV AI Kit (OAK) and launch a Kickstarter to raise over $500,000.
  • Saideep Talari landed his first job in computer vision after completing the PyImageSearch Gurus course and is now the CTO of a tech company with $5.1M in funding
  • Researcher Tuomo Hiippala was awarded a $30,500 grant to research how computer vision can be used to study visual culture, including social media images and photo archives
  • Machine learning engineer Yi Shern Yi Shern and the rest of the 123RF R&D team recently released a Visual Search feature on their site, allowing users to automatically find similar images among millions of images.

Adrian’s deep learning book is a great, in-depth dive into practical deep learning for computer vision. I found it to be an approachable and enjoyable read: explanations are clear and highly detailed. You’ll find many practical tips and recommendations that are rarely included in other books or in university courses. I highly recommend it, both to practitioners and beginners.

François CholletCreator of Keras

I am a firm believer that everything can be explained to anybody with the right words. Adrian Rosebrock excels in this art, and solves complex problems in only a few lines of code, going straight to the point and making it so clear. He makes deep learning so accessible and so exciting!

Aline BrokmannData Scientist at AstraZeneca

Is This Course Right for You?

PyImageSearch University is for students, researchers, hobbyists, and professionals who want on-demand access to tutorials on the latest and most advanced computer visions, deep learning, and and OpenCV topics available. We have found PyImageSearch University to be an especially good fit if you.

  • You are interested in getting started with computer vision and machine learning .
  • You want a job as a programmer and want to snag recruiters’ attention with a PyImageSearch certifications to show you’re proficient in Computer Vision.
  • You are an undergrad student doing your final graduation project and want to program an application to impress your professors or enhance your resume.
  • You are an MSc or PhD student working on your thesis/dissertation and need practical, hands-on education to complement what you learn in textbooks and research papers.
  • You are an entrepreneur in the computer vision/deep learning space and want basic coding skills to better understand and support your programmers.
  • You are a programming hobbyist and are ready to start programming your own applications with a versatile, easy-to-learn language.

Get 94 Professional Certificates of Completion

After successfully completing each class, you'll receive an official certificate of completion that you can add to your resume or embed directly on your LinkedIn profile.

Are The Courses in University Worth My Time and Money?

💰 Choose Your Plan 💰

Each plan contains Bonus Offerings worth $2,160! 💸

  • Bonus 1: Always look like a computer vision and deep learning genius with production-ready code from over 540 projects ($400 USD value) and 34 bonus advanced concept courses to give you an advantage in the job market ($900 USD value).
  • Enjoy 10 or more new courses annually ($310 USD/year), Colab notebooks for each lesson ($50 USD value), and Certificates for each course ($500 USD value)
  • Bonus 2: Engineering email support for your tough questions about our code
PyImageSearch University Lifetime

Unlock all resources with a one-time payment. Access all the 86 inside PyImageSearch University for less than $31/course!

You will get:

  • Lifetime access to PyImageSearch University
  • Brand new courses released every month, ensuring you can keep up with state-of-the-art techniques
  • 115 hours of on-demand video
  • 86 courses on essential computer vision, deep learning and OpenCV topics
  • 94 Certificates of Completion
  • 540 tutorials and downloadable resources
  • Pre-configured Jupyter Notebooks in Google Colab for 338 PyImageSearch tutorials
  • Run all code examples in your web browser - works on Windows, macOS, and Linux (no dev environment configuration required!)
  • Access to centralized code repos for all 348 tutorials on PyImageSearch
  • Easy one click downloads for code, datasets, pre-trained models, etc.
  • Access on mobile, laptop, desktop
$2,397 The most complete computer vision and deep learning education available online today.
PyImageSearch University

Take your education to the next level. Access all the 86 inside PyImageSearch University for less than $5/course!

You will get:

  • Brand new courses released every month, ensuring you can keep up with state-of-the-art techniques
  • 115 hours of on-demand video
  • 86 courses on essential computer vision, deep learning and OpenCV topics
  • 94 Certificates of Completion
  • 540 tutorials and downloadable resources
  • Pre-configured Jupyter Notebooks in Google Colab for 338 PyImageSearch tutorials
  • Run all code examples in your web browser - works on Windows, macOS, and Linux (no dev environment configuration required!)
  • Access to centralized code repos for all 348 tutorials on PyImageSearch
  • Easy one click downloads for code, datasets, pre-trained models, etc.
  • Access on mobile, laptop, desktop
$345/year
Perfect to learn the basics of Deep Learning and get started with real world applications.

The Best Computer Vision & Deep Learning Instruction You'll Find Anywhere--Guaranteed

If you aren't blown away by the breadth and quality of what you learn in PyImageSearch University, just send us an email to let us know and we'll provide a full refund up to 30 days after your purchase.

I absolutely love it. Far better than so many dumb, unorganized, and impractical courses present all over the internet.”

Mohammed Ehsan Ur RahmanDirector of Program and Operations at Sardhaar VDO

I have taken PyImageSearch's courses in the past and highly recommend them as good learning to gain quick knowledge.”

Brian TremaineSr. Technical Fellow

Questions?

I already have a PyImageSearch University account. How do I login?

Thank you for being a member of PyImageSearch University! You can login here.

Do I need any programming experience before joining PyImageSearch University?

We assume 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 inside PyImageSearch University.

Do I need to know anything about computer vision, deep learning, or OpenCV to get started in PyImageSearch University?

No. The courses inside PyImageSearch University will teach you computer vision, deep learning, and OpenCV. As long as you have basic programming experience you will be successful inside PyImageSearch University.

What happens after I purchase?

After you purchase you will be able to login and immediately access any code downloads, Jupyter Notebooks, video tutorials, courses, certificates of completion, etc.

Do I need any special software or hardware?

No. All of our courses, coding exercises, etc. can be completed inside your browser using our pre-configured Jupyter Notebooks running in Google Colab. If you prefer to instead configure your local development environment, we provide install instructions as well.

How will I be charged?

For monthly and yearly memberships, you will be charged on a recurring monthly or yearly basis, depending on your subscription type, starting from the sign-up date. You can cancel at any time.

There are no recurring payments for the lifetime membership — you will have access to PyImageSearch University at no additional cost.

Can I upgrade my account from one membership to another?

Yes! Simply select the membership you would like to upgrade to and join.

Can I pause/cancel my account?

Yes. Once you login, click your profile icon, followed by “Settings” and “Billing Info”. From there you can edit your payment method or cancel/pause your membership.

Do you offer bulk PyImageSearch University memberships to businesses, colleges, etc.?

Yes! Just send me a message via my contact form and we can schedule a call to discuss getting your organization access to PyImageSearch University.