Opera3dvectorfields
The CIFAR-10 dataset is a collection of 50,000 training images and 10,000 test images, each labeled with the name of one of 10 predefined classes. The CIFAR-100 dataset is 100 times larger than the CIFAR-10 dataset and is composed of 100 classes.
machine learning datasets | GitHub | Making data science easy. See the tens of thousands of datasets available on Kaggle for free. Note that you need a Kaggle account to view these datasets and download them.
Mining for the best model on the Kaggle platform. You can't download these results unless you have a Kaggle account.
Machine Learning Algorithms | GitHub | Complete reference of machine learning algorithms.
Distributed Machine Learning | GitHub | The famous Distributed Learning paper.
Matrix Algebra | GitHub | Useful matrix algebra routines for machine learning.
Constrained optimization | GitHub | Useful algorithms to solve constrained optimization problems in machine learning.
L-BFGS for constrained optimization | GitHub | L-BFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) is an algorithm that approximates the true Hessian for constrained optimization.
Recipes | GitHub | Useful recipes for machine learning.
Handwritten digit recognition using HOG and LBP | GitHub | Recognition of handwritten digit using HoG and LBP.
Suppose there are 30 images of handwritten digit 0, 9, 4, and 3, as shown in Figure 12-9. If you wanted to train an algorithm to recognize handwritten digit, one option might be the following:
  1. **Separate the handwritten digit images into two sets: the training set (15 images) and the test set (15 images).**
  2. **Load the images into Python, and compute the HOG features.**
For each image, you should get a set of HOG feature vectors.
  3. **Sum all the HOG feature vectors from the training set and normalize the sum by the number of images in the training set.**
  4. **Multiply all the HOG feature vectors from the test set by the same scalar, and add the result to the sum computed in step 3.**
By doing this, you are computing the difference between the feature vectors of the training set and the test set. The more similar they are, the more this difference is
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