Udemy - Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs Course Coupon Free Download

Udemy - Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs Course Coupon Free Download 2019-07-26

Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs
Udemy Course Free Download


Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs



Go from beginner to Expert in using Deep Learning for Computer Vision (Keras & Python) completing 28 Real World Projects
What you'll learn

  • 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.
  • 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+
  • Learn how to create, label, annotate, train your own Image Datasets, perfect for University Projects and Startups
  • Learn how to use OpenCV with a FREE Optional course with almost 4 hours of video
  • Learn how to use CNNs like U-Net to perform Image Segmentation which is extremely useful in Medical Imaging application
  • Learn how to use TensorFlow's Object Detection API and Create A Custom Object Detector in YOLO
  • Learn Facial Recognition with VGGFace
  • Learn to use Cloud GPUs on PaperSpace for 100X Speed Increase vs CPU
  • Learn to Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance

Course content

– Introduction
  • Course Introduction
– Introduction to Computer Vision & Deep Learning
  • Introduction to Computer Vision & Deep Learning
  • What is Computer Vision and What Makes it Hard
  • What are Images?
  • Intro to OpenCV, OpenVINO™ & their Limitations
– Setup Your FREE Deep Learning Development Virtual Machine
  • Setting up your Deep Learning Virtual Machine (Download Code, VM & Slides here!)
  • Optional - Troubleshooting Guide for VM Setup & for resolving some MacOS Issues
  • Optional - Manual Setup of Ubuntu Virtual Machine
  • Optional - Setting up a shared drive with your Host OS
– Handwriting Recognition, Simple Object Classification OpenCV Demo
  • Get Started! Handwriting Recognition, Simple Object Classification OpenCV Demo
  • Experiment with a Handwriting Classifier
  • Experiment with a Image Classifier
  • OpenCV Demo – Live Sketch with Webcam
– OpenCV3 Tutorial (OPTIONAL) - Live Sketches, Identify Shapes & Face Detection
  • Setup OpenCV
  • What are Images?
  • How are Images Formed
  • Storing Images on Computers
  • Getting Started with OpenCV - A Brief OpenCV Intro
  • Grayscaling - Converting Color Images To Shades of Gray
  • Understanding Color Spaces - The Many Ways Color Images Are Stored Digitally
  • Histogram representation of Images - Visualizing the Components of Images
  • Creating Images & Drawing on Images - Make Squares, Circles, Polygons & Add Text
  • Transformations, Affine And Non-Affine - The Many Ways We Can Change Images
  • Image Translations - Moving Images Up, Down. Left And Right
  • Rotations - How To Spin Your Image Around And Do Horizontal Flipping
  • Scaling, Re-sizing and Interpolations - Understand How Re-Sizing Affects Quality
  • Image Pyramids - Another Way of Re-Sizing
  • Cropping - Cut Out The Image The Regions You Want or Don't Want
  • Arithmetic Operations - Brightening and Darkening Images
  • Bitwise Operations - How Image Masking Works
  • Blurring - The Many Ways We Can Blur Images & Why It's Important
  • Sharpening - Reverse Your Images Blurs
  • Thresholding (Binarization) - Making Certain Images Areas Black or White
  • Dilation, Erosion, Opening/Closing - Importance of Thickening/Thinning Lines
  • Edge Detection using Image Gradients & Canny Edge Detection
  • Perspective & Affine Transforms - Take An Off Angle Shot & Make It Look Top Down
  • Mini Project 1 - Live Sketch App - Turn your Webcam Feed Into A Pencil Drawing
  • Segmentation and Contours - Extract Defined Shapes In Your Image
  • Sorting Contours - Sort Those Shapes By Size
  • Approximating Contours & Finding Their Convex Hull - Clean Up Messy Contours
  • Matching Contour Shapes - Match Shapes In Images Even When Distorted
  • Mini Project 2 - Identify Shapes (Square, Rectangle, Circle, Triangle & Stars)
  • Line Detection - Detect Straight Lines E.g. The Lines On A Sudoku Game
  • Circle Detection
  • Blob Detection - Detect The Center of Flowers
  • Mini Project 3 - Counting Circles and Ellipses
  • Object Detection Overview
  • Mini Project # 4 - Finding Waldo (Quickly Find A Specific Pattern In An Image)
  • Feature Description Theory - How We Digitally Represent Objects
  • Finding Corners - Why Corners In Images Are Important to Object Detection
  • Histogram of Oriented Gradients - Another Novel Way Of Representing Images
  • HAAR Cascade Classifiers - Learn How Classifiers Work And Why They're Amazing
  • Face and Eye Detection - Detect Human Faces and Eyes In Any Image
  • Mini Project 6 - Car and Pedestrian Detection in Videos
– Neural Networks Explained in Detail
  • Neural Networks Chapter Overview
  • Machine Learning Overview
  • Neural Networks Explained
  • Forward Propagation
  • Activation Functions
  • Training Part 1 – Loss Functions
  • Training Part 2 – Backpropagation and Gradient Descent
  • Backpropagation & Learning Rates – A Worked Example
  • Regularization, Overfitting, Generalization and Test Datasets
  • Epochs, Iterations and Batch Sizes
  • Measuring Performance and the Confusion Matrix
  • Review and Best Practices
– Convolutional Neural Networks (CNNs) Explained in Detail
  • Convolutional Neural Networks Chapter Overview
  • Convolutional Neural Networks Introduction
  • Convolutions & Image Features
  • Depth, Stride and Padding
  • ReLU
  • Pooling
  • The Fully Connected Layer
  • Training CNNs
  • Designing Your Own CNN
– Build CNNs in Python using Keras - Handwriting Recognition (MNIST)
  • Building a CNN in Keras
  • Introduction to Keras & Tensorflow
  • Building a Handwriting Recognition CNN
  • Loading Our Data
  • Getting our data in ‘Shape’
  • Hot One Encoding
  • Building & Compiling Our Model
  • Training Our Classifier
  • Plotting Loss and Accuracy Charts
  • Saving and Loading Your Model
  • Displaying Your Model Visually
  • Building a Simple Image Classifier using CIFAR10
– What CNNs 'see' - Learn to do Filter Visualizations, Heatmaps and Salience Maps
  • Introduction to Visualizing What CNNs 'see' & Filter Visualizations
  • Saliency Maps & Class Activation Maps
  • Saliency Maps & Class Activation Maps
  • Filter Visualizations
  • Heat Map Visualizations of Class Activations
– Data Augmentation: Build a Cats vs Dogs Classifier
  • Data Augmentation Chapter Overview
  • Splitting Data into Test and Training Datasets
  • Train a Cats vs. Dogs Classifier
  • Boosting Accuracy with Data Augmentation
  • Types of Data Augmentation
– Confusion Matrix, Classification Report & Viewing Misclassifications
  • Introduction to the Confusion Matrix & Viewing Misclassifications
  • Understanding the Confusion Matrix
  • Finding and Viewing Misclassified Data
– Types of Optimizers, Learning Rates & Callbacks: Build a Fruit Classifier
  • Introduction to the types of Optimizers, Learning Rates & Callbacks
  • Types Optimizers and Adaptive Learning Rate Methods
  • Keras Callbacks and Checkpoint, Early Stopping and Adjust Learning Rates that Pl
  • Build a Fruit Classifier
– Batch Normalization & Build LeNet, AlexNet: Build a Fashion/Clothes Classifier
  • Intro to Building LeNet, AlexNet in Keras & Understand Batch Normalization
  • Build LeNet and test on MNIST
  • Build AlexNet and test on CIFAR10
  • Batch Normalization
  • Build a Clothing & Apparel Classifier (Fashion MNIST)
– ImageNet in Keras (VGG16/19, InceptionV3, ResNet50) - Advanced Image Classiers
  • Chapter Introduction
  • ImageNet - Experimenting with pre-trained Models in Keras (VGG16, ResNet50, Mobi
  • Understanding VGG16 and VGG19
  • Understanding ResNet50
  • Understanding InceptionV3
– Transfer Learning and Fine Tuning: Build a Flower and Monkey Breed Classifier
  • Chapter Introduction
  • What is Transfer Learning and Fine Tuning
  • Build a Monkey Breed Classifier with MobileNet using Transfer Learning
  • Build a Flower Classifier with VGG16 using Transfer Learning
– Design Your Own CNN - LittleVGG: Build a Simpsons Character Classifier
  • Chapter Introduction
  • Introducing LittleVGG
  • Simpsons Character Recognition using LittleVGG
– Advanced Activation Functions and Initializations
  • Chapter Introduction
  • Dying ReLU Problem and Introduction to Leaky ReLU, ELU and PReLUs
  • Advanced Initializations
– Deep Surveillance: Build a Facial Emotion, Age & Gender Recognition System
  • Chapter Introduction
  • Build an Emotion, Facial Expression Detector
  • Build Emotion/Age/Gender Recognition in our Deep Surveillance Monitor
– Image Segmentation & Medical Imaging in U-Net: Find Nuclei in Images
  • Chapter Overview on Image Segmentation & Medical Imaging in U-Net
  • What is Segmentation? And Applications in Medical Imaging
  • U-Net: Image Segmentation with CNNs
  • The Intersection over Union (IoU) Metric
  • Finding the Nuclei in Divergent Images
– Principles of Object Detection
  • Chapter Introduction
  • Object Detection Introduction - Sliding Windows with HOGs
  • R-CNN, Fast R-CNN, Faster R-CNN and Mask R-CNN
  • Single Shot Detectors (SSDs)
  • YOLO to YOLOv3
– TensorFlow Object Detection API
  • Chapter Introduction
  • TFOD API Install and Setup
  • Experiment with a ResNet SSD on images, webcam and videos
  • How to Train a TFOD Model
– Object Detection with YOLO & Darkflow: Build a London Underground Sign Detector
  • Chapter Introduction
  • Setting up and install Yolo DarkNet and DarkFlow
  • Experiment with YOLO on still images, webcam and videos
  • Build your own YOLO Object Detector - Detecting London Underground Signs
– DeepDream & Neural Style Transfers: Make AI Generated Art
  • Chapter Introduction
  • DeepDream – How AI Generated Art All Started
  • Neural Style Transfer
– Generative Adversarial Networks (GANs): Age Faces to 60+ Age with our Age-cGAN
  • Generative Adverserial Neural Networks Chapter Overview
  • Introduction To GANs
  • Mathematics of GANs
  • Implementing GANs in Keras
  • Face Aging GAN
– Face Recognition with VGGFace
  • Basic Face Recognition using LittleVGG CNN
  • Face Matching with VGGFace
  • Face Recognition using WebCam & Identifying Friends TV Show Characters in Video
– The Computer Vision World
  • Chapter Introduction
  • Alternative Frameworks: PyTorch, MXNet, Caffe, Theano & OpenVINO
  • Popular APIs Google, Microsoft, ClarifAI Amazon Rekognition and others
  • Popular Computer Vision Conferences & Finding Datasets
  • Building a Deep Learning Machine vs. Cloud GPUs
– BONUS - Build a Credit Card Number Reader
  • Step 1 - Creating a Credit Card Number Dataset
  • Step 2 - Training Our Model
  • Step 3 - Extracting A Credit Card from the Background
  • Step 4 - Use our Model to Identify the Digits & Display it onto our Credit Card
– BONUS - Use Cloud GPUs on PaperSpace
  • Why use Cloud GPUs and How to Setup a PaperSpace Gradient Notebook
  • Train a AlexNet on PaperSpace
– BONUS - Create a Computer Vision API & Web App Using Flask and AWS
  • Install and Run Flask
  • Running Your Computer Vision Web App on Flask Locally
  • Running Your Computer Vision API
  • Setting Up An AWS Account
  • Setting Up Your AWS EC2 Instance & Installing Keras, TensorFlow, OpenCV & Flask
  • Changing your EC2 Security Group
  • Using FileZilla to transfer files to your EC2 Instance
  • Running your CV Web App on EC2
  • Running your CV API on EC2
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!
======================================================
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 spent months developing a proper and complete learning path.
  • 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:
  1. Handwritten Digit Classification using MNIST
  2. Image Classification using CIFAR10
  3. Dogs vs Cats classifier
  4. Flower Classifier using Flowers-17
  5. Fashion Classifier using FNIST
  6. Monkey Breed Classifier
  7. Fruit Classifier
  8. Simpsons Character Classifier
  9. Using Pre-trained ImageNet Models to classify a 1000 object classes
  10. Age, Gender and Emotion Classification
  11. Finding the Nuclei in Medical Scans using U-Net
  12. Object Detection using a ResNet50 SSD Model built using TensorFlow Object Detection
  13. Object Detection with YOLO V3
  14. A Custom YOLO Object Detector that Detects London Underground Tube Signs
  15. DeepDream
  16. Neural Style Transfers
  17. GANs - Generate Fake Digits
  18. GANs - Age Faces up to 60+ using Age-cGAN
  19. Face Recognition
  20. Credit Card Digit Reader
  21. Using Cloud GPUs on PaperSpace
  22. Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance!
And OpenCV Projects such as:
  1. Live Sketch
  2. Identifying Shapes
  3. Counting Circles and Ellipses
  4. Finding Waldo
  5. Single Object Detectors using OpenCV
  6. Car and Pedestrian Detector using Cascade Classifiers
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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