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

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) :
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 :
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 :
Convolutional Neural Networks Chapter Overview
Convolutional Neural Networks Introduction
Convolutions & Image Features
Depth, Stride and Padding
The Fully Connected Layer
Training CNNs
Designing Your Own CNN

Build CNNs in Python using Keras :
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' - Visualizations :
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

Build LeNet, AlexNet in Keras, Batch Normalization: Build a Fashion 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 & using pre-trained Models in Keras (VG16, VG19, InceptionV3, ResNet50) :
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 Flower Classifier with VGG16 using Transfer Learning
Build a Monkey Breed Identified with MobileNet 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)

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

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