Object Recognition: Deep Learning and Machine Learning for Computer Vision
Object recognition is enabling innovative systems like self-driving cars, image based retrieval, and autonomous robotics. The machine learning and deep learning these systems rely on can be difficult to train, evaluate, and compare.
In this webinar we explore how MATLAB addresses the most common challenges encountered while developing object recognition systems. This webinar will cover new capabilities for deep learning, machine learning and computer vision.
We will use real-world examples to demonstrate:
Training models using large image datasets
Training deep neural networks from scratch
Using transfer learning to re-use trained deep networks for new tasks
Exploring the tradeoffs between machine learning and deep learning
Related Resources:
What is Computer Vision? https://bit.ly/3NDmjbc
Get the example code used in this video: https://bit.ly/3VnTTZP
Chapters:
0:00 What is object recognition and when do I want to use it?
0:31 Demo #1: Scene Classification
0:43 The Machine Learning Workflow for Object Recognition
6:46 Classification Learner App for experimentation with different machine learning algorithms
9:00 Export an object recognition model from the Classification Learner App
10:27 Demo #1 Takeaways
11:22 The Deep Learning Workflow for Object Recognition
12:45 Demo #2: Fine-tune a pre-trained deep learning model (transfer learning)
18:00 Visualizing and removing mis-identified images from training data
19:06 Transfer Learning
19:53 Real-world object recognition with the transfer learned model and a deployable video player
20:30 Demo #2 Takeaways
21:36 Demo #3: Deep Learning and Machine Learning combined approach for object recognition
25:03 Demo #3 Conclusion
25:13 Machine Learning vs. Deep Learning object recognition overall comparison
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