Day 4

I finished both of the machine learning tutorials today. In the morning, I continued work on the first tutorial. I sped up the non-linear Support Vector Machine (SVM) that had been a problem the previous day by running the data through it without performing parameter searches, as I had been attempting to do before. This made the process faster because in order to do parameter searches, the program had to test multiple parameters using the SVM. However, I was able to perform both grid parameter searches and randomized parameter searches on the linear SVM, which is significantly faster than the non-linear SVM. (I am now also able to perform a search using the non-linear SVM, but it takes about half an hour.) I then used MinMaxScaler and StandardScalar to transform the data, making the predictions of the SVMs more accurate. After that, I applied a Principal Component Analysis (PCA) and an Independent Component Analysis (ICA) to the data. These transform the data into lower dimensions to improve the efficiency of the SVMs. That concluded the first tutorial.
The second tutorial went much more quickly for me than the first. I learned about five different types of autoencoders, which are data compression algorithms that usually use neural networks. I implemented and trained the five different types of autoencoders presented through the tutorial, beginning with a simple autoencoder and finishing with one that "denoises" images, which means to remove static randomly applied to the input images. After I finished the second tutorial, I did have much time left, but I did some reading about neural networks.

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