Found 4 result(s) for "Jupyter"! Click on the links for more details
Distributed_Analytics_of_US_Residential_Zoning
This is a project that aims to do distributed analytics using clusters using a spatial dataset. Our goal with this project was to analyze the impact of single family rresidential zoning in the US and correlate it to quality of life measures in an effort to dissuade a segreggation of zoning types and promote inclusivity. We hoped to be able to compare the results against data from other countries that have more includive zoning laws, but this was not possible due to constraints on data availability and language barriers. For the distributed component, we are using a cluster of 10 machines that are managed by Yarn. To do the processing of data and calculations, we applied Spark using Java and Gradle. The data itself was stored using HDFS and totaled to ~3.2 GB. For more detail on our motivation, procedures, project structure, and results, please reference the latex file or the presentation in the GitHub repo.
Japanese-To-English Translator
This is a translator program I built using Python that will take in a sentence in Japanese and translate the parts of the sentence into English and return an English translation. The original senetence is first split into various parts of speech using Japanese particles, which determines the transitivity of the verb if present and if a copula is present. Each part of speech is further broken down depending on the characters, length, conjugation, pairing, and use. This program does utilize the googletrans library as making a translator is a near impossible task, especially without the use of machine learning. The main feature of the program is to take the individual parts of the sentence, translate it using a csv with common words in Japanese, match it to a part of speech in English, and return that to the user. To learn more, click on the github icon next to the title of the project.
Handwritten Letters to 3D-Printed Braille Letters
This is a team project for my CS370 class that involved the use of a Raspberry Pi to communicate and coordinate two external devices. I acted as the team leader and inititated the project, managed the repository for the project, and coordinated the tasks of each member. The goal of the project was to use a camera to take a picture of some handwritten text, which is then uploaded to the Raspberry Pi. Using Python, we would preprocess the image, and use OpenCV to find and extract contours, leading to individual letters of specific size. The letters would then be sequentially classified by a convolutional neural network trained using the EMNIST dataset on handwriting. After this, the letters are combined using stl models into a single 3D model along the y-direction. We used slic3r to slice the stl file into gcode using the shell and sent that to the Raspberry Pi using ssh and sftp. The pi would then serially send each line of gcode to the printer to be printed.
Data Augmentation Using Generative Adversarial Networks
This was a team project for my CS345 class, where I worked with a teammate to research and use GANs as a way to improve the accuracy of classifiers. We first researched neural networks and the workings of GANs to understand how the model functions. Then, we used TensorFlow as the framework to implement the GAN model to generate data that could be augmented to the test set. We trained the generator on various splits of the original training data and noted how effective the augmentation was. This was compared to the baseline accuracy of just the original data on a SVC classifier to train and test. The use case for this was to improve the data on which to test as a mean of improving the accuracy of the classifier with better data produced by the GAN.