by Annie Phan, Drew Solomon, Yuyang Li, and Roma Coffin

Overview:

Recommendation systems have recently increased in popularity as they have proven to be successful in enhancing user-experience by using consumer data to develop personalized preferences to customers. Deep learning models in recommendation systems have become quite prevalent because they overcome limitations of other approaches and many times increase prediction accuracy. In this post, we summarize our approaches in generating movie recommendations based on users or movie information using deep learning. We describe our process to improve the performance of the transformer and implement more deep learning applications for movie…

by Annie Phan, Drew Solomon, Yuyang Li, and Roma Coffin

Overview:

Continuing on from our previous post, our main objective now is to implement new deep learning architectures for collaborative filtering for our recommendation task application. In particular, we aim to develop a transformer and tune it.

In our last blog post we discussed that we want to incorporate deep learning models into recommendation systems. Our goals include finding new tasks and building better movie recommendation systems that more accurately provide personalized content for the modern consumer. We also went over a brief overview of the MovieLens dataset, the associated…

by Annie Phan, Drew Solomon, Yuyang Li, and Roma Coffin

Project Overview:

Recommendation systems use consumer data to develop personalized preferences to customers. Tech companies have honed in on this strategy as it has proven to be successful in enhancing user-experience, popular examples include restaurant suggestions on Grubhub, playlist suggestions on Spotify, product suggestions on Amazon, or movie suggestions on Netflix. Recommendation systems typically use clustering, nearest neighbor, or matrix factorization techniques. Deep learning models have recently increased in popularity though to overcome limitations of these methods and increase prediction accuracy.

We are accessing the MovieLens dataset which consists of…

by Annie Phan, Drew Solomon, and Roma Coffin

Overview:

Image classification using deep learning may help identify diseased plants efficiently and prevent crop loss (Makerere University AI Lab, 2020). In this post, we summarize our approaches to classify cassava leaf disease using deep learning. We describe our EDA, data augmentation, model selection, fine-tuning, and hyperparameter tuning. Finally, we identify possible next steps to further improve our accuracy.

In our first blog post we introduced the Kaggle competition and the Cassava Leaf Data, went over our EDA process, and attempted to classify cassava leaf diseases. From our EDA, we determined the class balance (shown above) and examined leaf images…

by Annie Phan, Drew Solomon, and Roma Coffin

Overview:

Continuing on from our previous post, our main objective now is to improve upon the results from our baseline model. In our last blog post we went over our EDA process as well as our initial attempt in classifying cassava leaf diseases. With our initial VGG16 baseline model, we achieved a validation accuracy of 66.3% and training accuracy of 65.4%. (Using a simple majority classifier, the baseline was 61.5%.) Here, we improve upon our baseline model by using fine-tuning with VGG16, ResNet, and DenseNet models.

TFRecords:

For this project, we used…

by Annie Phan, Drew Solomon, and Roma Coffin

Overview:

Cassava is a key crop for food security across Sub-Saharan Africa. Yet, viral diseases threaten cassava yields, and are costly to detect manually. Thus, image recognition using deep learning may help identify diseased cassava plants efficiently and prevent crop loss.

Leveraging deep learning, our goal is to group cassava leaf image photos to five different categories using a dataset consisting of 21,367 images with labels of each category. The first four categories are a specific type of disease the plant may have and the fifth category is recognizing a healthy plant…

Hypothesis Testing is commonly used when trials for vaccine testing are performed. With all the recent vaccine news for Covid-19, I wanted to shed light on the general hypothesis testing process for vaccines. In this article, I will go over the following topics:

· Why is Hypothesis Testing Even Needed for Vaccines?

· Overview of Hypothesis Testing for Vaccines

· Trade Off between Type I vs Type II Errors

· Common Problems with Hypothesis Testing in Vaccines

Why is Hypothesis Testing Even Needed for Vaccines?

It is imperative to test a vaccine before it is accessible because researchers want to ensure that the vaccine is effective in stopping…

Roma Coffin

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