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| Archived
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4 - Simplifying Image Processing Using Dimensionality Reduction\31 - Reading and Preprocessing Images.mp4
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4 - Simplifying Image Processing Using Dimensionality Reduction\30 - Autoencoders.mp4
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4 - Simplifying Image Processing Using Dimensionality Reduction\27 - Sparse Representations Using Dictionary Learning.mp4
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4 - Simplifying Image Processing Using Dimensionality Reduction\25 - Module Overview.mp4
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4 - Simplifying Image Processing Using Dimensionality Reduction\26 - Dictionary Learning.mp4
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4 - Simplifying Image Processing Using Dimensionality Reduction\28 - Convolution Kernels.mp4
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4 - Simplifying Image Processing Using Dimensionality Reduction\29 - Feature Detection Using Convolution Kernels.mp4
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4 - Simplifying Image Processing Using Dimensionality Reduction\33 - Summary and Further Study.mp4
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4 - Simplifying Image Processing Using Dimensionality Reduction\32 - Designing and Training an Autoencoder.mp4
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1 - Course Overview\01 - Course Overview.mp4
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2 - Representing Images as Features for Machine Learning\10 - Block Views and Pooling.mp4
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2 - Representing Images as Features for Machine Learning\02 - Module Overview.mp4
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2 - Representing Images as Features for Machine Learning\11 - Denoising Images.mp4
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2 - Representing Images as Features for Machine Learning\05 - Image Preprocessing to Build Robust Models.mp4
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2 - Representing Images as Features for Machine Learning\04 - Representing Images for Machine Learning.mp4
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2 - Representing Images as Features for Machine Learning\06 - Working with Images as Arrays.mp4
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2 - Representing Images as Features for Machine Learning\07 - Representing Pixels in Images.mp4
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2 - Representing Images as Features for Machine Learning\12 - Normalization and ZCA Whitening.mp4
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2 - Representing Images as Features for Machine Learning\13 - Image Augmentation Using Weather Transforms.mp4
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2 - Representing Images as Features for Machine Learning\08 - Working with Color and Color Spaces.mp4
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2 - Representing Images as Features for Machine Learning\09 - Resizing, Rescaling, Rotating, and Flipping Images.mp4
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2 - Representing Images as Features for Machine Learning\03 - Prerequisites and Course Outline.mp4
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2 - Representing Images as Features for Machine Learning\14 - Module Summary.mp4
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3 - Detecting Features and Text in Images\21 - Feature Detection Using DAISY Descriptors.mp4
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3 - Detecting Features and Text in Images\17 - Key Points and Descriptors.mp4
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3 - Detecting Features and Text in Images\23 - Optical Character Recognition Using Tesseract.mp4
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3 - Detecting Features and Text in Images\24 - Module Summary.mp4
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3 - Detecting Features and Text in Images\16 - Feature Detection and Its Importance.mp4
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3 - Detecting Features and Text in Images\19 - Scale Invariant Feature Transform (SIFT), DAISY, and Histogram of Oriented Gradients (HOG).mp4
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3 - Detecting Features and Text in Images\18 - Applying Keypoint Preserving Transformations.mp4
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3 - Detecting Features and Text in Images\15 - Module Overview.mp4
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3 - Detecting Features and Text in Images\22 - Feature Detection Using Histogram of Oriented Gradients.mp4
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3 - Detecting Features and Text in Images\20 - Feature Detection and Extraction Using SIFT.mp4
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4 - Simplifying Image Processing Using Dimensionality Reduction |
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