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Assignments for Geoffrey Hinton's Neural Net Course on Coursera, translated from (gross)Matlab into (beautiful)Python.

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Project Description

Assignments for Geoffrey Hinton's Neural Net Course on Coursera, translated from Matlab into Python.

  • assignments 2-4 are quite different than what is presented in the course, as they were refactored into logical classifiers (adapted from the sklearn framework).
  • more work could certainly be done to remove redundancy between assignments, especially between 3 and 4.
  • course can be found here: https://www.coursera.org/course/neuralnets

Assignment 1

  • Implements linear Perceptron for two class problem

Assignment 2

  • Implements a basic framework for training neural nets with mini-batch gradient descent for a language model.
  • Assignment covers hyperparameter search and observations through average cross entropy error.
    • i.e. number of training epochs, embedding and hidden layer size, training momentum

Assignment 3

  • Trains a simple Feedforward Neural Network with Backpropogation, for recognizing USPS handwritten digits.
  • Assignment looks into efficient optimization, and into effective regularization.
  • Recognizes USPS handwritten digits.

Assignment 4

  • Trains a Feedforward neural network with pretraining using Restricted Boltzman Machines (RBMs)
  • The RBM is used as the visible-to-hidden layer in a network exactly like the one made in programming assignment 3.
  • The RBM is trained using Contrastive Divergence gradient estimator with 1 full Gibbs update, a.k.a. CD-1.
  • Recognizes USPS handwritten digits.

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Assignments for Geoffrey Hinton's Neural Net Course on Coursera, translated from (gross)Matlab into (beautiful)Python.

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  • Python 83.9%
  • Jupyter Notebook 16.1%