Download - TensorFlow Tutorial Part1
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Overview
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Part1: TensorFlow Tutorials
Handling images
Logistic regression
Multi-layer perceptron
Part2: Advances in convolutional neural networks
CNN basics
Four CNN architectures (AlexNet, VGG, GoogLeNet, ResNet)
Application1: Semantic segmentation
Application2: Object detection
Convolutional neural network
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Before going on
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Terminologies are Important!
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Goal of (most of) Deep Learning
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Most of the deep learning or machine learning algorithms can be viewed as a mapping from a vector space to another.
In other words, it is just numbers to numbers.
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Input data
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Output / Class / Label
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Cat[1 0 0 0]
Dog[0 1 0 0]
Cow[0 0 1 0]
Horse[0 0 0 1]
One-hot coding
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Training / Learning
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Epoch / Batch size / Iteration
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One epoch: one forward and backward pass of all training data
Batch size: the number of training examples in one forward and backward pass
One iteration: number of passes
If we have 55,000 training data, and the batch size is 1,000. Then, we need 55 iterations to complete 1 epoch.
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Part1: TensorFlow tutorial
Handling images
Logistic regression
Multi-layer perceptron
Convolutional neural network
![Page 10: TensorFlow Tutorial Part1](https://reader031.vdocuments.us/reader031/viewer/2022013107/58ac5c301a28ab8e258b6187/html5/thumbnails/10.jpg)
Part1: TensorFlow tutorial
Handling images
Logistic regression
Multi-layer perceptron
Convolutional neural network
![Page 11: TensorFlow Tutorial Part1](https://reader031.vdocuments.us/reader031/viewer/2022013107/58ac5c301a28ab8e258b6187/html5/thumbnails/11.jpg)
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Load packages
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![Page 13: TensorFlow Tutorial Part1](https://reader031.vdocuments.us/reader031/viewer/2022013107/58ac5c301a28ab8e258b6187/html5/thumbnails/13.jpg)
Specify folders containing images
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Load images
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Check loaded images
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Divide into train and test sets
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Save!
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Plot to check
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Plot to check
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