•Machine Learning for OpenCV Advanced Methods and Deep Learning
•The Course Overview
•Understanding and Implementing Bayesian Classifier
•Classifying Emails Using Naive Bayes Classifier
•Understanding Unsupervised Learning and kmeans Clustering
•Understanding ExpectationMaximization
•Compressing Color Spaces Using kmeans
•Classifying Handwritten Digits Using kmeans
•Organizing Clusters as a Hierarchical Tree
•Understanding and Implementing Perceptron
•Understanding and Implementing Multilayer Perceptrons
•Getting Acquainted with Deep Learning
•Classifying Handwritten Digits
•Understanding Ensemble Methods
•Combining Decision Trees into a Random Forest
•Using Random Forests for Face Recognition
•Implementing AdaBoost
•Combining Different Models into a Voting Classifier
•Evaluating a Model
•Understanding CrossValidation
•Estimating Robustness Using Bootstrapping
•Assessing the Significance of Our Results
•Tuning Hyperparameters with Grid Search
•Chaining Algorithms Together to Form a Pipeline
•HandsOn Deep Learning for Computer Vision
•A HighLevel Overview of Deep Learning
•Installing Keras and TensorFlow
•Building a CNN Based Autoencoder to Denoise Images
•An Introduction to ImageNet Dataset and VGG Model
•Using a PreTrained VGG Model
•Summary and Whats Next
•Introduction to GANs
•Building GANs to Learn MNIST Dataset
•An Introduction to Object Detection and YOLO
•Installing and Setting Up Keras Implementation of YOLO
•Using a PreTrained YOLO Model for Object Detection
•An Introduction to Neural Style Transfer
•Using Keras Implementation of Neural Style Transfer
•Test your knowledge
•Object Detection and Recognition Using Deep Learning in OpenCV
•How to Work with Images in OpenCV
•Enhancement and Filtering Operations in OpenCV
•Saving Images Accessing Camera
•Image Transformations
•Computer Vision Algorithms
•Working with Object Recognition
•Features and Descriptors
•Feature Matching and Homography
•Building an Application
•Getting Started with Neural Networks
•Architecture of a Convolutional Neural Network CNN
•Starting with Caffe
•Implementing Deep Learning Using OpenCV and Caffe
•Defining Problem Statement
•Designing an Algorithm for the Problem
•Training the Network Using Labeled Data
•Classification Problem
•Problem Definition and Gathering Dataset
•Modeling Appropriate Algorithm
•Moving from Algorithm to Code
•Results and Analysis
•Machine Learning for OpenCV – Advanced Methods and Deep Learning
•Understanding Unsupervised Learning and k-means Clustering
•Understanding Expectation-Maximization
•Compressing Color Spaces Using k-means
•Classifying Handwritten Digits Using k-means
•Understanding Cross-Validation
•Hands-On Deep Learning for Computer Vision
•A High-Level Overview of Deep Learning
•Summary
•Using a Pre-Trained VGG Model
•Summary and What’s Next?
•Using a Pre-Trained YOLO Model for Object Detection
•How to Work with Images in OpenCV?
•Saving Images, Accessing Camera
•Architecture of a Convolutional Neural Network (CNN)
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