Udemy – Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs
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What you’ll learn
- Learn by completing 26 advanced computer vision projects including Emotion, Age & Gender Classification, London Underground Sign Detection, Monkey Breed, Flowers, Fruits , Simpsons Characters and many more!
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Learn Advanced Deep Learning Computer Vision Techniques such as Transfer Learning and using pre-trained models (VGG, MobileNet, InceptionV3, ResNet50) on ImageNet and re-create popular CNNs such as AlexNet, LeNet, VGG and U-Net.
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Understand how Neural Networks, Convolutional Neural Networks, R-CNNs , SSDs, YOLO & GANs with my easy to follow explanations
- Become familiar with other frameworks (PyTorch, Caffe, MXNET, CV APIs), Cloud GPUs and get an overview of the Computer Vision World
- Learn how to use the Python library Keras to build complex Deep Learning Networks (using Tensorflow backend)
- Learn how to do Neural Style Transfer, DeepDream and use GANs to Age Faces up to 60+
Requirements
- Basic programming knowledge is a plus but not a requirement
- High school level math, College level would be a bonus
- Atleast 20GB storage space for Virtual Machine and Datasets
- A Windows, MacOS or Linux OS
Description
Deep Learning Computer Vision™ Use Python & Keras to implement CNNs, YOLO, TFOD, R-CNNs, SSDs & GANs + A Free Introduction to OpenCV3.
If you want to learn all the latest 2019 concepts in applying Deep Learning to Computer Vision, look no further – this is the course for you! You’ll get hands the following Deep Learning frameworks in Python:
- Keras
- Tensorflow
- TensorFlow Object Detection API
- YOLO (DarkNet and DarkFlow)
- OpenCV
All in an easy to use virtual machine, with all libraries pre-installed!
Apr 2019 Updates:
- How to setup a Cloud GPU on PaperSpace and Train a CIFAR10 AlexNet CNN almost 100 times faster!
- Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance!
Mar 2019 Updates:
Newly added Facial Recognition & Credit Card Number Reader Projects
- Recognize multiple persons using your webcam
- Facial Recognition on the Friends TV Show Characters
- Take a picture of a Credit Card, extract and identify the numbers on that card!
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Computer vision applications involving Deep Learning are booming!
Having Machines that can ‘see‘ will change our world and revolutionize almost every industry out there. Machines or robots that can see will be able to:
- Perform surgery and accurately analyze and diagnose you from medical scans.
- Enable self-driving cars
- Radically change robots allowing us to build robots that can cook, clean and assist us with almost any task
- Understand what’s being seen in CCTV surveillance videos thus performing security, traffic management and a host of other services
- Create Art with amazing Neural Style Transfers and other innovative types of image generation
- Simulate many tasks such as Aging faces, modifying live video feeds and realistically replace actors in films
Huge technology companies such as Facebook, Google, Microsoft, Apple, Amazon, and Tesla are all heavily devoting billions to computer vision research.
As a result, the demand for computer vision expertise is growing exponentially!
However, learning computer vision with Deep Learning is hard!
- Tutorials are too technical and theoretical
- Code is outdated
- Beginners just don’t know where to start
That’s why I made this course!
- I teach all key concepts logically and without overloading you with mathematical theory while using the most up to date methods.
- I created a FREE Virtual Machine with all Deep Learning Libraries (Keras, TensorFlow, OpenCV, TFODI, YOLO, Darkflow etc) installed! This will save you hours of painfully complicated installs
- I teach using practical examples and you’ll learn by doing 18 projects!
Projects such as:
- Handwritten Digit Classification using MNIST
- Image Classification using CIFAR10
- Dogs vs Cats classifier
- Flower Classifier using Flowers-17
- Fashion Classifier using FNIST
- Monkey Breed Classifier
- Fruit Classifier
- Simpsons Character Classifier
- Using Pre-trained ImageNet Models to classify a 1000 object classes
- Age, Gender and Emotion Classification
- Finding the Nuclei in Medical Scans using U-Net
- Object Detection using a ResNet50 SSD Model built using TensorFlow Object Detection
- Object Detection with YOLO V3
- A Custom YOLO Object Detector that Detects London Underground Tube Signs
- DeepDream
- Neural Style Transfers
- GANs – Generate Fake Digits
- GANs – Age Faces up to 60+ using Age-cGAN
- Face Recognition
- Credit Card Digit Reader
- Using Cloud GPUs on PaperSpace
- Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance!
And OpenCV Projects such as:
- Live Sketch
- Identifying Shapes
- Counting Circles and Ellipses
- Finding Waldo
- Single Object Detectors using OpenCV
- Car and Pedestrian Detector using Cascade Classifiers
So if you want to get an excellent foundation in Computer Vision, look no further.
This is the course for you!
In this course, you will discover the power of Computer Vision in Python, and obtain skills to dramatically increase your career prospects as a Computer Vision developer.
Who this course is for:
- Programmers, college students or anyone enthusiastic about computer vision and deep learning
- Those wanting to be on the forefront of the job market for the AI Revolution
- Those who have an amazing startup or App idea involving computer vision
- Enthusiastic hobbyists wanting to build fun Computer Vision applications
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