Deep-Learning-for-Computer-Vision

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Deep-Learning-for-Computer-Vision

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DL4CV book

Deep-Learning-for-Computer-Vision 최신버젼 다운로드

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# Deep-Learning-for-Computer-Vision

This repository contains the examples given in the book "Deep Learning for Computer Vision with Python by Adrian Rosebrock".

### Table of contents

##### [01_KNN_classifier:](01_KNN_classifier)
**Summary:** A simple image processor was built, an image dataset was loaded into the memory and the k-Nearest Neighbor algorithm was developed. The disadvantages of k-NN algorithm was discussed and moved on to parameterized learning.
             
##### [02_Linear_example:](02_Linear_example)
**Summary:** A worked example is solved here. The random state of the python script to generate the weights that would lead to the correct classification was purposely set. We discuss the requirement of an optimization algorithm and go to next chapter.
             
##### [03_optimization_and_regularization:](03_optimization_and_regularization)
**Summary:** Implemented vanilla gradient descent and stochastic gradient descent from sratch with L1 and L2 regularization.

##### [04_artificial_neural_networks:](04_artificial_neural_networks)
**Summary:** Develope back propagation algorithm for neural networks from scratch and demonstrated its advantages over a simple perceptron algorithm. Tested the algorithm with MNIST and CIFAR-10 dataset, discussed how standard neural networks fail to accurately classify images.

##### [05_Convolutions:](05_convolutions)
**Summary:** Discussed how convolutions work in a convolutional neural network and implemented it from scratch for blurring, sharpening, edge detection, embossing, etc.

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