English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 0h 46m | 1.45 GB
Deep learning is a fairly recent and hugely popular branch of artificial intelligence (AI) that finds patterns and insights in data, including images and video. Its layering and abstraction give deep learning models almost human-like abilities—including advanced image recognition. Using OpenCV—a widely adopted computer vision software—you can run previously trained deep learning models on inexpensive hardware and generate powerful insights from digital images and video. In this course, instructor Jonathan Fernandes introduces you to the world of deep learning via inference, using the OpenCV Deep Neural Networks (dnn) module. You can get an overview of deep learning concepts and architecture, and then discover how to view and load images and videos using OpenCV and Python. Jonathan also shows how to provide classification for both images and videos, use blobs (the equivalent of tensors in other frameworks), and leverage YOLOv3 for custom object detection.
Topics include:
- Deep learning for OpenCV
- Viewing images and video in OpenCV
- Working with blobs in the dnn module
- Image classification
- Video classification
- YOLOv3
Table of Contents
1 Generate insights from digital images and video with OpenCV
2 What you should know before watching this course
3 Install Python and Anaconda
4 Create a virtual environment
5 Install a text editor
6 What is deep learning
7 What is OpenCV
8 Deep learning for OpenCV
9 Viewing images in OpenCV
10 Working with color channels
11 Viewing videos in OpenCV
12 Overview of the dnn process
13 Working with blobs
14 Classification for an image – Getting the classes
15 Classification for an image – Inference
16 Classification for a video
17 YOLOv3
18 YOLOv3 in action
19 Next steps
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