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\nVision Deep Learning\n

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\n\nPublisher:\n\nAPMonitor\n\n\n
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\nIn computer vision, deep learning has proven useful to extract patterns from images. Deep learning uses a neural network and optimization to relate features (pixels) to a desired label. As opposed to Cascade Classifiers, deep learning...\nIn computer vision, deep learning has proven useful to extract patterns from images. Deep learning uses a neural network and optimization to relate features (pixels) to a desired label. As opposed to Cascade Classifiers, deep learning does not need specialized preprocessing of the image to develop application-specific features.

The pixels from the image are processed through multiple linear and nonlinear layers to predict an output. Deep learning generally requires many thousands of labeled examples to learn.

A Convolutional Neural Network (CNN) transforms the input image with a specialized connectivity structure. It stacks multiple stages of feature extractors. The higher stages compute more global, invariant features with a classification layer at the end. Feed-forward feature extraction convolves input with learned filters, transforms with non-linearity (sigmoid, hyperbolic tangent, rectified linear units), performs spatial pooling, and finally normalizes to create a feature map. With convolution the dependencies are local, translation is invariant, and there are few parameters (filter weights and stride). The supervised training of convolutional filters is performed by back-propagating classification error.

Deep learning has made significant progress in face recognition, image classification, speech recognition, text-to-speech generation, handwriting transcription, medical diagnosis, self-driving cars, digital assistants, advertising, search queries, and social recommendations. While there has been progress with deep learning, many of the big questions of intelligence have not been answered or properly formulated.\n
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Resource Details

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Curator Rating
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Educator Rating
Not yet Rated
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Media Length
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12:51
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Grade
10th - Higher Ed
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Subjects
STEM
3 more...
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Media Type
Instructional Videos
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Source:
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\nAPMonitor.com\n\n
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Date
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2022
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Language
English
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Audiences
For Teacher Use
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Usage Permissions
Fine Print: Educational Use
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Additional Materials

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Concepts

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case studies, number 3, landmarks, faces, pixels
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Additional Tags

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apmonitor, mediapipe, mesh, face, deep learning, detection, computer vision, starting point, good job, media pipe, face detection, video capture, face mesh, complete face, recognize faces, trained, confidence, case, video, number, matplotlib, read, detecting, import, convert, image, features, multitask, give
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Classroom Considerations

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Best For: Explaining a topic
Video is ad-free
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Educator Ratings

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