Day 26 (Final Presentations)

This morning, we had our final presentations for the internship. All of the presentations seemed very good, even though I had already watched them in practice. My presentation was third, and I felt pretty good about how it went. After the presentations, we received our letters of recommendation and our certificates. I talked to Dr. Kanan and some of the other interns, then I showed my family the lab upstairs. Today was a positive end to an excellent summer experience.

Comments

Post a Comment

Popular posts from this blog

Day 5

I began the first experiment of my project today. But first, I prepared by doing some reading on the topic of hyperspectral image classification. I read a paper written by Ron and Dr. Kanan about self-taught feature learning. It discussed two self-taught learning frameworks: the multiscale ICA and the stacked convolutional autoencoder. Much of it was difficult for me to understand because I am new to this topic, but I learned more about self-taught machine learning programs. After I finished reading, I began my first experiment. The first step was to document already written machine learning code and to figure out what that code was doing. I read about the standard Python documentation style, then I began to document the code. This proved to be challenging, because this code implemented many methods from outside of itself, which meant I had to understand those methods as well. Today I learned that understanding and following code that was written by someone else can be just as challeng
Read more

Day 13

Today I continued tests for my classifier experiment. I worked on finding the optimal parameters for the Multi-Layer Perceptron (MLP) classifier. I was trying to determine the best hidden layer shape and weight decay. Hidden layer shape is how many dimensions (layers) of neurons (nodes used for machine learning) there are, and how many neurons are in each dimension. I only began with the knowledge that there would be a maximum of three layers in the hidden layer shape. Weight decay is a value that decreases the weight values after each training iteration to prevent overfitting to the training data. I knew that the best value for weight decay would be 1e-4, 5e-4, or 0. I wrote a program to test different combinations of hidden layer shapes and weight decay values. While these tests were running, I documented some of the classifiers with which I was working, including the MLP classifier. This process helped me to better understand MLP and the other classifiers because it made me work thr
Read more

Day 7

Today, I started by writing my abstract, which is a short summary of the project on which I am working this summer. (The abstract is included in my last post.) To write my abstract, I needed to develop a clear idea of my project. I found that summarizing it in writing actually helped me to better understand what I am doing as a whole. After completing and posting my abstract, I returned to my experiment. I first set out to solve the problem of incorrect dimensions in my data, which I had run into on the previous day. I took out the code that reshaped the data, because the data had to be in their original dimensions for the preprocessing methods to function correctly. However, this meant that I had to rewrite the code that split the data by labels into training and testing categories. I had to use an entirely different strategy than I had been using before. Instead of directly splitting the data into two arrays, I am now creating an array of the indices of the data values in the origina
Read more