By Jackson Brandberg, Ilina Mitra, and Alaena Roberds

Review of previous work:

Over the past few weeks, we have been working to improve sentence embeddings through deep learning to eventually improve message clustering in Prismia, an ed-tech virtual chat interface that allows students and instructors to have dynamic interactions over course material. In Part 1 of our blog series, we performed exploratory data analysis of the current clustering mechanism, created a testing dataset and evaluation metric for our efforts moving forward, and ran our baseline S-BERT model. In Part 2, we discussed some roadblocks we encountered in preprocessing our deep learning textbook, which we…


by Jackson Brandberg, Ilina Mitra and Alaena Roberds

Review of week 1:

Last week, we performed some preliminary exploratory data analysis as a first step to enhancing the Prismia, a virtual chat interface that allows students and instructors to have dynamic interactions over course material. You can read more about the steps we took then in Part 1 of our blog series. In short, we selected and created a test set of responses to train the existing clustering algorithm and calculated a baseline cosine-similarity score of 0.555. In this week’s post we are going to discuss the steps we took to fine tune this…


by Alaena Roberds, Ilina Mitra and Jackson Brandberg

Background and context:

Prismia is a virtual chat interface that allows students and instructors to have dynamic interactions over course material. It was mainly created to fill a gap in what most other classroom evaluators lack. Alternatives like iClicker and LearningCatalytics are pretty limited to only allowing instructors to ask multiple choice questions. Moreover, giving customizable feedback to individuals is very challenging on those platforms. Prismia offers a solution to those issues by allowing students and instructors to have more of a free-form conversation on the material, and ask questions during any points of confusion.


by Jackson Brandberg, Ilina Mitra, and Alaena Roberds

View our Google Colab notebook!

Review:

As introduced in the first blog post in this series, Cassava is an extremely important crop in Africa as it represents the second-largest provider of carbohydrates. As this crop is so vital, not only to the people who consume its carbohydrates but to the farmers on whose livelihood it depends, our goal of detecting and classifying Cassava Leaf Disease through machine learning is an important task. Part 1 of this series covered loading data from the Kaggle API, Exploratory Data Analysis (EDA), and a simple majority-classifier baseline…


by Ilina Mitra, Jackson Brandberg and Alaena Roberds

Review

In the first post of this series, we introduced the Cassava Leaf Dataset from Kaggle and our goal to use Convolution Neural Nets (CNNs) to classify images of cassava leaves as either healthy or as possessing a certain disease (of five different disease types). By the end of that post, we had loaded our data using the Kaggle API, done exploratory data analysis and created a baseline model which predicted every image as possessing Cassava Mosaic Disease (CMD) — the most populous class in our dataset. This model yielded an accuracy of…


by Jackson Brandberg, Ilina Mitra, and Alaena Roberds

Cassava is one of Africa’s most important plants. It is the second-largest provider of carbohydrates (Kaggle) and is crucial to the livelihood of a large percentage of African farmers. Given the subcontinent’s reliance on this root, viral diseases affecting cassava root have the potential to set back both the health of people across the continent as well as many local/national economies.

In this series of blog posts, we will document our progress completing Cassava Leaf Disease Classification through Kaggle. It is the hope of the project creators that we can leverage neural…


I was recently down a dark, social media rabbit hole when I came across this post:

Clearly in no rush to finish my work, and dedicated to beating this puzzle, I stared at the image for some time. But the image was tricking my brain and I grew frustrated. Sure, I wasn’t totally lost; I recognized fur on the right, and potentially more fur on the left, and I saw what looked like three eyes in the center. Were they eyes? I was fairly certain about a red, fleece-like material at the bottom of the image. But the puzzle pieces…

Alaena Roberds

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