Machine Learning and Deep Learning with OpenCV

Machine Learning and Deep Learning with OpenCV, available at $19.99, has an average rating of 3.4, with 63 lectures, 2 quizzes, based on 5 reviews, and has 65 subscribers.

You will learn about Grasp the advanced concepts of bootstrapping, boosting, voting, and bagging. Discover hidden structures in your data using k-means clustering. Encode, decode, and denoise images with autoencoders. Understand the structure and function of neural networks and CNN/pooling. Classify images with OpenCV using smart Deep Learning methods Detect objects in images with You Only Look Once (YOLOv3) Work with advanced imaging tools such as Deep Dream, Style Transfer, and Neural Doodle See functionalities in OpenCV that can be used for Object detection and recognition through Deep Learning Master use of OpenCV by practicing the creation of basic applications. This course is ideal for individuals who are This course is perfect for: or Python Developers who are already familiar with OpenCV and want to perform Machine Learning and Deep Learning with OpenCV will find this Course very helpful. It is particularly useful for This course is perfect for: or Python Developers who are already familiar with OpenCV and want to perform Machine Learning and Deep Learning with OpenCV will find this Course very helpful.

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Summary

Title: Machine Learning and Deep Learning with OpenCV

Price: $19.99

Average Rating: 3.4

Number of Lectures: 63

Number of Quizzes: 2

Number of Published Lectures: 63

Number of Published Quizzes: 2

Number of Curriculum Items: 65

Number of Published Curriculum Objects: 65

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Grasp the advanced concepts of bootstrapping, boosting, voting, and bagging.

Discover hidden structures in your data using k-means clustering.

Encode, decode, and denoise images with autoencoders.

Understand the structure and function of neural networks and CNN/pooling.

Classify images with OpenCV using smart Deep Learning methods

Detect objects in images with You Only Look Once (YOLOv3)

Work with advanced imaging tools such as Deep Dream, Style Transfer, and Neural Doodle

See functionalities in OpenCV that can be used for Object detection and recognition through Deep Learning

Master use of OpenCV by practicing the creation of basic applications.

Who Should Attend

This course is perfect for:

Python Developers who are already familiar with OpenCV and want to perform Machine Learning and Deep Learning with OpenCV will find this Course very helpful.

Target Audiences

This course is perfect for:

Python Developers who are already familiar with OpenCV and want to perform Machine Learning and Deep Learning with OpenCV will find this Course very helpful.

Machine Learning and Deep learning techniques, in particular, are changing the way computers see and interact with the World. From augmented and mixed-reality applications to just gathering data, these new techniques are revolutionizing a lot of industries. OpenCV is a cross-platform library using which we can develop real-time computer vision applications. It mainly focuses on image processing, video capture, and analysis including features like face detection and object detection. This course is designed to give you a hands-on learning experience by going from the basic concepts to the most current in-depth Deep Learning methods for Computer Vision in use today.

This comprehensive 3-in-1 course a step-by-step, project-based approach to skill up with techniques for detection and decoding of images with advanced concepts of Machine Learning and Deep Learning with OpenCV. Initially, you’ll not only understand, perform, and experiment with Machine Learning techniques but also load, store and visualize data using OpenCV and Python. You’ll also learn Deep Learning techniques commonly used for Computer Vision: from denoising to classification/similarity matching, image generation, and object detection. Finally, you’ll explore functionalities in OpenCV that can be used for Object detection and recognition through Deep Learning as well as master use of OpenCV by practicing the development of basic applications.

By the end of the course, you’ll skill up with techniques for detection and decoding of images with advanced concepts of Machine Learning and Deep Learning with OpenCV.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Machine Learning for OpenCV – Advanced Methods and Deep Learning, covers a practical introduction to the world of machine learning and image processing using OpenCV and Python. The course will also guide you through creating custom graphs and visualizations, and show you how to go from raw data to beautiful visualizations. By the end of this course, you will be ready to create your own ML system and will also be able to take on your own machine learning problems.

The second course, Hands-On Deep Learning for Computer Vision, covers how to generate, detect, and classify images faster and more accurately. In this course, you will be introduced to the concept of deep learning and a variety of popular and effective techniques for image classification, detection, segmentation, and generation. You will learn to build your own neural network and classify images accordingly. You will be taken through popular techniques such as Deep Dream (to generate psychedelic, surreal images), Style Transfer (to transfer styles between images), and Neural Doodle, to generate an image that matches a doodled sketch. By the end of this course, you will be able to use computer vision and deep learning to encode, classify, detect, and style images for the real world.

The third course, Object Detection and Recognition Using Deep Learning in OpenCV, covers OpenCV Object Recognition: Harness Deep Learning in OpenCV. This course teaches effective object recognition and its implementation with the powerful OpenCV libraries. You will learn how to enhance your OpenCV skills with deep learning. You will explore and master OpenCV for Object Recognition/Classification. The course explains all the necessary theory and concepts of computer vision, image processing, and machine learning. You also learn the practical application of OpenCV libraries. Its capabilities and functionality are shown along with a tutorial on how to set up a machine such that it’s able to use OpenCV in codes. You will start by seeing how to work with images in OpenCV, enhancement, and filtering in OpenCV. You will then move on to building an application which is capable of object recognition and performing homography. You will then move on to object classification and recognizing text in an image. In the end, you will be able to use object recognition algorithm which will be used by you for practical application.

By the end of the course, you’ll learn techniques for detection and decoding of images with advanced concepts of Machine Learning and Deep Learning with OpenCV.

About the Authors

Michael Beyeler is a Postdoctoral Fellow at the University of Washington in Seattle. His work lies at the intersection of neuroscience, computer vision, and machine learning. Michael is the author of two Packt books: OpenCV with Python Blueprints (2015) and Machine Learning for OpenCV (2017). He is an active contributor to several open-source software projects and has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android.

Jakub Konczyk has enjoyed programming professionally since 1995. He is a Python and Django expert and has been involved in building complex systems since 2006. He loves to simplify and teach programming subjects and share his knowledge with others. He first discovered Machine Learning when he was trying to predict real estate prices in one of the early stage startups he was involved in. He failed miserably. Then he discovered a much more practical way to learn Machine Learning that he would like to share with you in this course. It boils down to the Keep it simple! mantra.

Param Uttarwar has extensively worked in OpenCV and he has been in touch with OpenCV, ML and AI field. Previously, he worked on a similar kind of project where he developed software that aided users in learning image processing. It used OpenCV and Qt Libraries at its core and was deployed on the hardware. The author has worked out image processing algorithms on SBCs & FPGAs. The author has also taught these topics himself, so he is well acquainted with the practical problems faced during learning which are not written in books. He feels that he is the best suited for authoring this course.

Course Curriculum

Chapter 1: Machine Learning for OpenCV – Advanced Methods and Deep Learning

Lecture 1: The Course Overview

Lecture 2: Understanding and Implementing Bayesian Classifier

Lecture 3: Classifying Emails Using Naive Bayes Classifier

Lecture 4: Understanding Unsupervised Learning and k-means Clustering

Lecture 5: Understanding Expectation-Maximization

Lecture 6: Compressing Color Spaces Using k-means

Lecture 7: Classifying Handwritten Digits Using k-means

Lecture 8: Organizing Clusters as a Hierarchical Tree

Lecture 9: Understanding and Implementing Perceptron

Lecture 10: Understanding and Implementing Multilayer Perceptrons

Lecture 11: Getting Acquainted with Deep Learning

Lecture 12: Classifying Handwritten Digits

Lecture 13: Understanding Ensemble Methods

Lecture 14: Combining Decision Trees into a Random Forest

Lecture 15: Using Random Forests for Face Recognition

Lecture 16: Implementing AdaBoost

Lecture 17: Combining Different Models into a Voting Classifier

Lecture 18: Evaluating a Model

Lecture 19: Understanding Cross-Validation

Lecture 20: Estimating Robustness Using Bootstrapping

Lecture 21: Assessing the Significance of Our Results

Lecture 22: Tuning Hyperparameters with Grid Search

Lecture 23: Chaining Algorithms Together to Form a Pipeline

Chapter 2: Hands-On Deep Learning for Computer Vision

Lecture 1: The Course Overview

Lecture 2: A High-Level Overview of Deep Learning

Lecture 3: Installing Keras and TensorFlow

Lecture 4: Building a CNN Based Autoencoder to Denoise Images

Lecture 5: Summary

Lecture 6: An Introduction to ImageNet Dataset and VGG Model

Lecture 7: Using a Pre-Trained VGG Model

Lecture 8: Summary and What’s Next?

Lecture 9: Introduction to GANs

Lecture 10: Building GANs to Learn MNIST Dataset

Lecture 11: Summary and What’s Next?

Lecture 12: An Introduction to Object Detection and YOLO

Lecture 13: Installing and Setting Up Keras Implementation of YOLO

Lecture 14: Using a Pre-Trained YOLO Model for Object Detection

Lecture 15: Summary and What’s Next?

Lecture 16: An Introduction to Neural Style Transfer

Lecture 17: Using Keras Implementation of Neural Style Transfer

Lecture 18: Summary

Chapter 3: Object Detection and Recognition Using Deep Learning in OpenCV

Lecture 1: The Course Overview

Lecture 2: How to Work with Images in OpenCV?

Lecture 3: Enhancement and Filtering Operations in OpenCV

Lecture 4: Saving Images, Accessing Camera

Lecture 5: Image Transformations

Lecture 6: Computer Vision Algorithms

Lecture 7: Working with Object Recognition

Lecture 8: Features and Descriptors

Lecture 9: Feature Matching and Homography

Lecture 10: Building an Application

Lecture 11: Getting Started with Neural Networks

Lecture 12: Architecture of a Convolutional Neural Network (CNN)

Lecture 13: Starting with Caffe

Lecture 14: Implementing Deep Learning Using OpenCV and Caffe

Lecture 15: Defining Problem Statement

Lecture 16: Designing an Algorithm for the Problem

Lecture 17: Training the Network Using Labeled Data

Lecture 18: Classification Problem

Lecture 19: Problem Definition and Gathering Dataset

Lecture 20: Modeling Appropriate Algorithm

Lecture 21: Moving from Algorithm to Code

Lecture 22: Results and Analysis

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 1 votes

2 stars: 1 votes

3 stars: 0 votes

4 stars: 1 votes

5 stars: 2 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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