CIFAR-10 Dataset

The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. see http://www.cs.toronto.edu/~kriz/cifar.html

Contains 6 files, each with 10000 shuffled images of 10 labeled classes.

The images are of size 32x32 pixels with 3 color channels (RGB). The intensity in each channels is encoded as unsigned 8-bit integers 0...255.

0. airplane
1. automobile
2. bird
3. cat
4. deer
5. dog
6. frog
7. horse
8. ship
9. truck

The files are pickles (python2) with keys ['data', 'labels', 'batch_label', 'filenames']

Start Neural Networks training:

• Fully-Connected Neural Network (simple neural network + with regularization)
• Convolutional Neural Network (few convolutional layers + fully connected layers)
• Deep Convolutional Neural Network (CNN with regularization, shortcuts, batch normalization, advanced initialization etc.)