Convolutional Neural Networks (Computer vision)
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MNIST (sota)


State of the art MNIST

A CNN model which achieves 99.7% accuracy. It makes use of Vgg16 model. I've used data augmentation, batch normalization, dropout, maxpooling . The model has been finetuned. Accuracy on training set = 99.42 and accuracy on validation set = 99.45. The model doesn't overfit at all. Three models have been used:

  • Linear model
  • Single dense layers
  • Vgg style CNN


  • keras==1.2.2
  • Python 2.7
Use model.summary() to gather more intuiton about convolutional layers


Dogs and cats classification

Made use of Vgg16 model(which won Imagenet competition in 2014).

Models has been finetuned in order to classify image as dog or cat.

Download data:


Deep Residual Network

ConvNet architecture with Skip connections. Can make ConvNet more deeper with increased acccuracy.

Acquiring this repo

$ cd ~
$ git clone
$ cd machine-learning-programs


Contributions are always welcome in the form of pull requests with explanatory comments. You can Fork this repo and send in pull request to expand the repository if they can fit into this repository.