No 1 online resource in computer vision and deep learning as voted by Neptune.ai
BRAND NEW COURSE: Deep Learning Course — JOIN NOW!
Deep Learning Course
The fastest path to Deep Learning from the number 1 online Deep Learning resource.
Getting started with Deep Learning is now easy with these short, step-by-step tutorials. This deep learning course is a specially curated course to provide you with everything to get started.
Inside this course, you will learn what is Deep Learning, various optimization algorithms and also how to train your first Deep Learning Model.
The best part?
You will be able to build an end-to-end Deep Learning project when you finish this course!
Trusted by members of top machine learning companies and schools. Join them in computer vision mastery.
Check out a full lesson from inside the course...
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
Curious about what you’ll learn?
Deep Learning Course will teach you all the fundamentals of Deep Learning from first principles to actually training your first model. You will learn via practical, hands-on projects (with lots of code), so you can not only develop your own models but feel confident while doing so.
Inside the course, you will learn:
- What is Deep Learning
- Image Classification
- Parameterized Learning
- Optimization and Regularization
- Implementing a Multi Layer Perceptron
- Backpropogation
- Training in Tensorflow and Keras
- Convolutional Neural Networks from scratch
- Recognize handwritten digits
- Saving and loading your model
- Distributed training with Google TPUs
- Basic real world applications
Deep Learning Course Syllabus
20 Courses • 82 Classes • 27h 51m 21s Lectures
Deep Learning 101 — NNs and Parameterized Learning
5 lessons, 0h 56m 35s
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
Deep Learning 102 — Optimization Methods and Regularization
4 lessons, 1h 13m 10s
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
Deep Learning 103 — Neural Network Fundamentals
6 lessons, 2h 03m 30s
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
Deep Learning 104 — Convolutional Neural Networks (CNNs)
3 lessons, 0h 49m 28s
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
Deep Learning 105 — Hands-on Experience with CNNs
7 lessons, 1h 42m 49s
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
Deep Learning 120 — Regression with CNNs
3 lessons, 1h 13m 07s
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
Deep Learning 125 — Data Pipelines with tf.data
3 lessons, 1h 19m 52s
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
Deep Learning 130 — Hyperparameter Tuning
4 lessons, 1h 19m 24s
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
PyTorch 101 — Fundamentals of PyTorch
5 lessons, 1h 28m 45s
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
PyTorch 102 — Intermediate PyTorch for CV techniques
3 lessons, 1h 17m 24s
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
PyTorch 103 — Advanced PyTorch techniques
2 lessons, 0h 12m 00s
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
Autoencoders 101 — Intro to Autoencoders
4 lessons, 1h 36m 13s
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
Siamese Networks 101 — Intro to Siamese Networks
4 lessons, 1h 52m 23s
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
Image Adversaries 101 — Intro to Image Adversaries
5 lessons, 2h 26m 52s
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
Object Detection 101 — Easy Object Detection
7 lessons, 1h 51m 16s
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
Object Detection 201 — Fundamentals of Deep Learning Object Detection
4 lessons, 2h 29m 02s
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
Object Detection 202 — Bounding Box Regression
2 lessons, 1h 01m 06s
Bounding Box Regression (31:45)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Multi-class Bounding Box Regression (29:21)
Final exam
Face Applications 101 — Face Detection
4 lessons, 1h 06m 51s
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
Face Applications 102 — Fundamentals of Facial Landmarks
4 lessons, 0h 51m 56s
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
Face Recognition 101 — Fundamentals of Facial Recognition
3 lessons, 0h 59m 38s
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
Don’t Just Learn. Get Hands-on Practice
Inside this course, we’ll use Colab Notebooks, a free interactive Python programming interface from Google.
This course will enable you to run all code examples in your web browser with guided tutorials and specific practice examples. And it works on Windows, macOS, and Linux (no dev environment configuration required)!
You're probably wondering ...
“Is this course right for me?”
This course is for developers, students, researchers, and hobbyists who want to learn how to successfully master Deep Learning (and have at least some programming/scripting experience).
If any of these descriptions fit you ... this course is for you.
- You are a developer who needs to learn how to apply Deep Learning to a project.
- You write code at your day job and are motivated to stand out from your coworkers by learning Deep Learning.
- You are an undergrad student doing your final graduation project and building Deep Learning Application to impress your classmates and teachers.
- You are a MSc or PhD student working on your thesis/dissertation and need a practical, hands-on education to complement what you learn in textbooks and research papers.
- You are a computer science teacher who wants to teach your students how to get started with Deep Learning.
- You are a computer vision researcher who is just tipping your toe in Deep Learning and need practical education to get started.
- You are an entrepreneur in the computer vision/deep learning space and see a gap in the market that Deep Learning could help solve.
- You are a "computer vision hobbyist" who hacks around with OpenCV and now wants to learn how to Deep Learning.
Includes a “Deep Learning Course” Certificate of Completion
To receive the certificate, you will need to complete all lessons and quizzes associated with the course.
After completing all lessons/quizzes, you will receive your certificate and be able to embed it directly on your LinkedIn profile, thereby demonstrating your Deep Learning skills.
Are Your Courses Worth It?
Deep Learning Course Only
Take your education to the next level. Access the entire Deep Learning Course.
You will get:
- The most comprehensive step by step tutorials for Deep Learning.
- Comprehensive knowledge about popular Deep Learning architectures used today.
- Saving and loading a pretrained model for future use.
- Real world projects like face detection and traffic sign classification
- In depth coverage of popular libraries like OpenCV, Keras, TensorFlow and PyTorch
- Industry standard optical character recognition with Tesseract
- Object detection from basics to mastery
- Creating and managing Deep Learning pipelines
- All source code files
- Certificate of Completion (after successfully passing the final exam)
Deep Learrning Course + PyImageSearch University
One year subscription to PyImageSearch University.Brand new courses released every month, ensuring you can keep up with state-of-the-art techniques.
You will get:
- 39h 44m on-demand video
- 28 courses on essential computer vision, deep learning and OpenCV topics
- 28 Certificates of Completion
- 304 tutorials and downloadable resources
- Pre-configured Jupyter Notebooks in Google Colab for 200+ 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 500+ tutorials on PyImageSearch
- Easy one click downloads for code,datasets, pre-trained models etc.
- Access on mobile,laptop,desktop
Enjoy a 100% money-back guarantee.
After taking this course, if you haven't learned how to build and train your first Deep Learning model, 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 one one hand. Our readers are satisfied, and we're sure you will be too.
I absolutely love it. Far better than so many dumb, unorganized, and impractical courses present all over the internet.”
I have taken PyImageSearch's courses in the past and highly recommend them as good learning to gain quick knowledge.”
Questions?
Is this course right for me?
This course is specially designed for anyone who is short on time but want to get familiar with the fundamentals of Deep Learning. Our team has curated the most detailed and comprehensive lessons from our huge gallery of deep learning courses and put them in this Deep Learning Course.The best part - You will be able to build an end-to-end Deep Learning project when you finish this course!
What bundle should I buy?
That depends on what your needs are. If you want to learn deep learrning and you don't need help with anything else, you can start with the one time course.
If you want a complete education on Deep Learning along with access to all our newly released courses, then opt for the yearly bundle.
Do I need any programming experience before taking this course?
Absolutely no programming experience is required! This course is perfect for Computer Vision beginners or hobbyists looking to level up their programming.
What happens after I purchase?
After your purchase, you will (1) receive an email receipt for your purchase, and (2) be able to access the course, code, datasets, etc., immediately.
What if I’m a beginner?
- It's okay if you are brand new to Computer Vision, mobile apps or coding in general! This course makes no assumptions on your prior experience with Python, programming, computer vision, or deep learning. In fact, this course is designed for you!
- You will be able to follow along, take practice quizzes and earn your certificate if you complete this beginner’s course.
What if I’m already experienced in Deep Learning?
You'll revisit Deep Learning from first principles.
You will be to understand optimization and regularization
You will train custom Keras and TensorFlow models on cloud GPU
In addition you'll also get a certificate that you can show off on LinkedIn or your resume.
What is your money-back guarantee policy?
We offer a 30-day Money-Back Guarantee on all orders. If you haven't learned Computer Vision for Mobile Apps after going through this course, then I don't want your money. Simply send us an email and ask for a refund up to 30 days after your purchase. With all the copies I've sold, I can count the number of refunds on one hand. My readers are satisfied, and I'm sure you will be too.
I'm just so busy right now…
Everyone has the same amount of time in a day — we all have 24 hours to work, spend time with our families, sleep, and have fun. If you're interested in studying Deep Learning, I challenge you to make it your goal. Take the time to invest in yourself and your education by grabbing a copy of Deep Learning Course.
Ask yourself, how much time are you wasting because:
- You lack the fundamentals of OpenCV, Computer Vision, and Deep Learning.
- You don’t understand what knobs and dials to tune to achieve high accuracy results.
- Your scripts error out, leaving you confused on how to proceed.
- You keep putting off finishing that big project
Deep Learning Course solves these problems so you can stop wasting your time and money following paths that only lead to failure — let us guide you to success!
I have another question.
If you have any other questions, please send me a message, and I'll get back to you ASAP.
© 2024 PyImageSearch. All Rights Reserved.