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followers: 2.0 following: 2.0 repos: 8.0 gists: 0.0

Name: Sreecharan Vanam

Type: User

Sreecharan Vanam's Projects

building-a-language-understanding-and-question-answering-system-from-open-ended-trivia-data

This project uses BERT to build a QA system fine-tuned on the SQuAD dataset, improving the accuracy and efficiency of question-answering tasks. We address challenges in contextual understanding and ambiguity handling to enhance user experience and system performance.

devportfolio

A lightweight, customizable single-page personal portfolio website template built with JavaScript and Sass

generating-realistic-and-diverse-textual-movie-reviews-with-imdb-dataset-leveraging-deep-learning

This project uses GPT-2 to generate realistic movie reviews from the IMDb dataset. By preprocessing data and fine-tuning the model, we achieved human-like text quality. The model's reviews were evaluated for coherence and diversity, showcasing GPT-2's potential in automated text generation.

improving-cifar-10-image-classification-with-diverse-architectures-using-ensemble-learning

This project uses an ensemble of CNN, RNN, and VGG16 models to enhance CIFAR-10 image classification accuracy and robustness. By combining multiple architectures, we significantly outperform single-model approaches, achieving superior classification performance.

prolog-based-graph-traversal-bfs-implementation

An implementation of graph traversal algorithms in Prolog, focusing on breadth-first search (BFS). The project finds the shortest path between nodes in a graph, covering both directed and undirected graphs with extensive test cases.

tokenization-and-categorization-using-ocaml-a-lexical-analysis-approach

An OCaml-based lexical analyzer that identifies and classifies tokens such as identifiers, operators, punctuation symbols, integer literals, and keywords. The project involves tokenizing input text, categorizing tokens, and printing them with their respective categories. Key functions include tokenize, is_alnum, is_punctuation, and print_tokens.

utilizing-multinomial-naive-bayes-for-enhanced-movie-genre-classification-and-analysis

This project uses the Multinomial Naive Bayes classifier to enhance movie genre classification based on metadata such as descriptions and ratings. Utilizing a dataset from Kaggle, it aims to improve content recommendation systems through accurate genre prediction.

visualizing-current-data-science-salary-trends-key-insights-for-job-seekers

This project analyzes data science salary trends using a Kaggle dataset. By examining factors like job titles, experience levels, and geographic regions, we provide insights to help job seekers and professionals understand salary patterns in the data science field.

sreecharanv Goto Github PK

Sreecharan Vanam's Projects

building-a-language-understanding-and-question-answering-system-from-open-ended-trivia-data

devportfolio

generating-realistic-and-diverse-textual-movie-reviews-with-imdb-dataset-leveraging-deep-learning

improving-cifar-10-image-classification-with-diverse-architectures-using-ensemble-learning

prolog-based-graph-traversal-bfs-implementation

tokenization-and-categorization-using-ocaml-a-lexical-analysis-approach

utilizing-multinomial-naive-bayes-for-enhanced-movie-genre-classification-and-analysis

visualizing-current-data-science-salary-trends-key-insights-for-job-seekers

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