Tag Archives: C++

C++ Code For Reading CIFAR-10 Dataset

The CIFAR-10 dataset can be found HERE. It is a very popular multi-channel image dataset for classifier training, as a simplify version of CIFAR-100, it is easier to use for newbies. Here’s a C++ version code for reading this dataset from .bin files into OpenCV matrices.

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A Simple Deep Network

During this spring break, I worked on building a simple deep network, which has two parts, sparse autoencoder and softmax regression. The method is exactly the same as the “Building Deep Networks for Classification” part in UFLDL tutorial. For better understanding it, I re-implemented it using C++ and OpenCV.  GENERAL OUTLINE Read dataset (including training data […]

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Poisson Blending II

I re-wrote the Poisson Blending code using C++ and OpenCV. About the Algorithm, see my Previous Poisson Blending post. This time, I just used the most stupid way, just solving the Poisson Equation. You can improve it by using advanced methods. About solving discrete Poisson Equation using Jacobi, SOR, Conjugate Gradients, and FFT, read THIS.   In […]

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Softmax Regression (with OpenCV)

This is the same algorithm with the previous SOFTMAX REGRESSION post. Because I’m going to try to build deeper neural networks for images, so as a review of OpenCV programming, I rewrote the Softmax regression code using OpenCV mat, instead of Armadillo. I used Matlab, Octave, Armadillo a lot these days, it is kind of […]

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C++ code for reading MNIST dataset

Here’s a code for reading MNIST dataset in C++, the dataset can be found HERE, and the file format is as well. Using this code, you can read MNIST dataset into a double vector, or an OpenCV Mat, or Armadillo mat. Feel free to use it for any purpose. (part of this code is stolen from […]

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Softmax Regression

WHAT IS SOFTMAX Softmax regression always as one module in a deep learning network, and most likely to be the last module, the output module. What is it? It is a generalized version of logistic regression. Just like logistic regression, it belongs to supervised learning, and the superiority is, the class label y can be more than […]

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K-Means Clustering

ABOUT UNSUPERVISED LEARNING In supervised learning problems, we deal with labeled data, means during the training process, we give our machine both Xs and Ys, by training, we ‘forge’ a system, which given new Xs, it is able to guess the output Ys. By using better algorithm and sophisticated methods, we can make the forged system […]

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Back Propagation Neural Networks

I used to thought that BP Neural Networks algorithm belongs to supervised learning, however, after learned about Sparse Autoencoder algorithm, I realized it can also be used for unsupervised learning (use the unlabeled data itself as both input and output). BP neural networks is the base of a lot of other advanced neural networks algorithm, […]

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Linear Regression study notes

I chose Prof. Yann LeCun‘s Deep Learning as one of my three attending courses this semester, because that I have no background of machine learning, so I also viewing Prof. Andrew Ng’s Machine Learning lectures online recent days. At the beginning of the lectures, it was about linear regression. I heard about some simple linear regression […]

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