Homework

Homework 1 - K Nearest Neighbors on MNIST database. Additional files: MNIST.

• K Nearest Neighbors (MATLAB Version)small version. Download students.zip for the files.

• K Nearest Neighbors (Python Version) small version. Download students_python.zip for the files.

Homework 2 - Gaussian Mixture Models.

• GMM small version.

Homework 3 - Linear and logistic regression from stanford.

• Linear regression

• Logistic regression

• Softmax regression

• Combined HW on Linear and Logistic Regression - Short Version. Additional files: Combined_HW_Linear_and_Logistic_Reg.zip.

• Linear Regression for Python (python file link).

Homework 4 - Sigmoid and Neural Networks: feedforward and backpropagation

• Homework on sigmoid function and neural networks.

Homework 5 - Neural networks

• Self-reading and exercises

  • Neural Networks and Deep Learning - Chapter 1

  • Neural Networks and Deep Learning - Chapter 2

• More exercises

  • Sparse autoencoder

  • Vectorization

• Neural Net implementation HW has three parts

  • part 1 (Basic Neural Net Implementation - MNIST)

  • part 2 (Regularization for Neural Net)

  • part 3 (Tensorflow2) or (Tensorflow and Pytorch)

Homework 6 - Neural networks

• Neural Networks and Deep Learning - Chapter 3 + Exercises.

• Elements of Information Theory, 2nd Edition - Chapters 1 and 2.

• Odd questions in homework 1 on Information Theory.

Homework 7 - Neural Networks and Restricted Bolzman Machine (RBM)

• Part 1: Neural Networks

• Part 2:RBM
  Additional files for the HW may find here

Homework 8 - Recurrent neural networks (RNN), Long-Short term memory (LSTM), Attention

• Homework on RNN

• Homework on Attention

Homework 9: Implement the RNN-LSTM for the database Penn Tree Bank (PTB).

Homework 10 - Tree distribution

• Homework 1 on Tree distribution

• Homework 2 on Tree distribution.

Homework 11 - Speaker recognition

• Homework on speaker recognition