Week 7 (July 8 –July 14) Progress

Started studying algorithms and mathematics for implementing the ClassificationNeuralNetwork class

This week, I started work on the ClassificationNeuralNetwork.m and fitcnet.m files. I also began studying the mathematics behind neural network classifiers. After reviewing the MATLAB documentation, I identified the following components for the ClassificationNeuralNetwork class:

  • Weights initializer: Layer weights are initialized using either 'glorot' or 'he'.
  • Biases initializer: Biases are initialized using either 'zeros' or 'ones'.
  • Loss function: Cross entropy.
  • Solver used: L-BFGS (Limited-memory quasi-Newton code for bound-constrained optimization).

Initially, I am focusing on getting the forward propagation to work properly. You can see my work here.

Resources I found helpful include:

  • "Neural Network from scratch in Python" by Sentdex (unfortunately, the playlist was never completed).
  • Andrew Ng's lecture, where he mentions that understanding the detailed mathematics behind the L-BFGS optimization technique is not necessary; we just need to know how to use it.
  • Andrew Ng's Machine Learning from Octave playlist, starting from lecture 401.

I successfully passed my Midterm Evaluation as part of GSoC.

I submitted my first Pull request, which was successfully merged into the Statistics repository.

Our next meeting is scheduled for July 23rd at 16:30 UTC. Any help related to the algorithm part is greatly appreciated. See you in the next blog. 😄

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