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adaptready

algo_hw8

algo_hw_8

blockbreaker

cuda_engine_image_classifier_

This project is aimed to convert a Tensorflow model built in Python into a Cuda Engine based on C++. This is done to reduce the latency of the code. Latency is a critical bottleneck for a lot of Deep Learning Projects. Majority of the tensorflow projects could not be materialized into real world solutions because the latency was impractical. However converting the trained model into a CUDA engine based in C++ could help us reduce the latency significantly. In this project we use fashion_MNIST dataset as an example. We train the dataset using a normal Tensorflow model and then save this model into an .uff file which is universally readable. We then use these weights to create a API based model in C++ and then feed this model the training images to calculate accuracy and most importantly latency.

facial-emotion-recognition

Using deep learning and keras

multiclass-object-detection-tensorflow

We create an object detection program that detect custom objects of multiple classes.

scientific_summary

We present a comparitive study of popular summarization models's performance on summarizing scientific documents after transferlearning.NLP project for CS6140 at Northeastern University.

smart-parking-system

This project is to design a smart parking system which can automatically allocate a parking space for a car through internet.

spell_variation_clustering

summarization

traffic_signal_detection