README.org

• Overview My code for the Deep Learning class exercises. There should be nothing proprietary in here. For the earlier exercises, I tried to create parallel implementations in Octave and NumPy. Later on, class-supplied helper code necessitated the use of Matlab (for now).

• Materials

• [[http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=DeepLearning][OpenClassroom Regression Tutorial]]
• [[http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial][UFLDF Tutorial Wiki]]

• Note

• The L-BFGS Matlab code is licensed by Stanford under a Creative Commons, Attribute, Non-Commercial license. Please read the [[http://ufldl.stanford.edu/wiki/index.php/Exercise:Sparse_Autoencoder#Sparse_autoencoder_implementation][details on the UFLDL wiki]].
• The MNIST digit data comes from [[http://yann.lecun.com/exdb/mnist/]].

• Questions ** UFLDL "Since J(W,b) is a non-convex function, gradient descent is susceptible to local optima; however, in practice gradient descent usually works fairly well." - [[http://ufldl.stanford.edu/wiki/index.php/Backpropagation_Algorithm][UFLDL/Backpropagation]]

Why? Is it almost convex? Are the local optima all of a similar quality? Are any of the variations (squared error / squared error + weight decay / squared error + weight decay + sparsity constraints) convex?

• Tasks ** Python *** TODO Get [[file:ufldf/stackedae_exercise/stackedae_exercise.py][stackedae_exercise.py]] to work. *** TODO Implement [[file:ufldf/linear_decoder_exercise][linear_decoder_exercise]]. *** TODO Implement [[file:ufldf/cnn_exercise][cnn_exercise]].