vthuynh2310 Goto Github PK

followers: 0.0 following: 0.0 repos: 23.0 gists: 0.0

Type: User

vthuynh2310's Projects

cerebral-microbleeds-detection

covid-19-cxr-ct-classification

covid19_detector

Using Deep Learning to detect Covid-19 from X-Ray/CT scans of patients

cranfield-vector-space-model

Implementation of a Vector Space Retrieval Model using TF-IDF and cosine similarity on the Cranfield document corpus

cup_public

Code repository for CUP: Cluster Pruning for Compressing Deep Neural Networks

document_processing

Tiền xử lý trong bài toán toán phân loại văn bản theo chủ đề áp dụng kỹ thuật naive bayes, có xử lý từ viết tắt

information-retrieval

Stemming, TF IDF, Precision, Recall, M AP

Engineered a complete information retrieval system with a Web Crawler, a Parser, an Indexer, a Search Engine and Pseudo Relevance Feedback in Python. Evaluated performance of BM25, TF/IDF, Query Likelihood and Lucene’s retrieval model using MAP, MRR, P@K, and Precision and Recall. Incorporated PageRank algorithm to improve search results by performing link analysis.

pneumonia-diagnosis-using-xrays-96-percent-recall

BEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional neural network and tranfer learning via inceptionV3. The images were of size greater than 1000 pixels per dimension and the total dataset was tagged large and had a space of 1GB+ . My work includes self laid neural network which was repeatedly tuned for one of the best hyperparameters and used variety of utility function of keras like callbacks for learning rate and checkpointing. Could have augmented the image data for even better modelling but was short of RAM on kaggle kernel. Other metrics like precision , recall and f1 score using confusion matrix were taken off special care. The other part included a brief introduction of transfer learning via InceptionV3 and was tuned entirely rather than partially after loading the inceptionv3 weights for the maximum achieved accuracy on kaggle till date. This achieved even a higher precision than before.