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Deep Learning

This repository contains my project notebooks for the Deep Learning course.


Notebooks

  • Implement a Two Layers Fully Connected Neural Network in Numpy by hand writing the Feedforward and Backpropagation functions.
  • Train and evaluate models on MNIST data set, Comparing the influences of:
    • Stochastic / Batch / Mini batch gradient descent
    • different hidden layer size
    • different learning rate
    • sigmoid / softmax output

  • Using Tensorboard to visualize the graph and training progress.
  • Re-build the FCNN model using Tensorflow.
  • Implement a LeNet-5 CNN model in Tensorflow
  • Perform some optimizations to get more than 99% of accuracy on MNIST.
    • Different Optimizer :Gradient Descent vs AdamOptimizer
    • Dropout




  • Build a Vanilla-RNN and a GRU by hand writing the formulas in Tensorflow.
  • Classify user comments from IMDb, Amazon, and Yelp to two classification(Negative / Positive).

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