This repo contains implementations of popular machine learning algorithms and some techniques that come along with them:

• Logistic Regression with regularization
• Multilayer Neural network with:
  • supported layer activations: ReLU, sigmoid, softmax
  • optimizers: gradient descent, GD with momentum, RMSProp, Adam
  • mini-batch training
  • adjustable number of hidden layers

A few IPython notebooks illustrate the results that these implementations can achieve and compares them with similar implementations in the Keras library.

• Dropout in the neural network
• Batch normalization in the neural network
• Tanh activation
• ConvNet implementation
• Some sequence models implementaions
• More notebooks experimenting with models implemented using Keras and TensorFlow.