Projects on Neural Networks for Mail Spam Detection - Cognitive Computing Systems (MEng), supervised by Prof. P. Maresca (2024)
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Updated
Jun 11, 2024 - Jupyter Notebook
Projects on Neural Networks for Mail Spam Detection - Cognitive Computing Systems (MEng), supervised by Prof. P. Maresca (2024)
This AI project aims to classify people's reviews from watching films using the IMDb dataset and an encoder-based transformer architecture, specifically the well-known LLM model BERT.
Identifying psychosis in teens using computer vision, EDA, and gesture analysis.
An NER model using BERT transformers identifies and classifies named entities in text by leveraging BERT's deep bidirectional understanding of language, making it highly effective for natural language processing tasks.
a dna sequence generation/classification using transformers
Machine learning model trained on 520K social media comments generating sentiment analysis based on usage of emojis in comments.
This project aims to provide developers and researchers with a powerful tool for working with text data, including tasks such as text summarization, topic modeling, named entity recognition (NER), translation, and speech-to-text conversion.
The "LLM Projects Archive" is a centralized GitHub repository, offering a diverse collection of Language Model Models projects. A valuable resource for researchers, developers, and enthusiasts, it showcases the latest advancements and applications in the realm of LLMs. Explore and contribute to the dynamic landscape of language model projects.
NLP - CS4120 @ Northeastern University | Final Project | Performing emotion classification on a kaggle dataset using models such as Logistic Regression, LSTM Neural Network, and DistilBERT, a transform-based model.
This is a repo of basic Machine Learning what I learn. More to go...
This project utilizes the power of BERT (Bidirectional Encoder Representations from Transformers) for sentiment analysis
A Notebook that demonstrates how to use the BART Transformer model to perform title generation from abstracts.
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.
Deep Learning, Attention, Transformers, BERT, GPT-2, GTP-3
We use NLP techniques like sentiment analysis and topic modelling to analyze large volumes of customer reviews and extract valuable insights that can aid businesses in decision making
Text classification model - Bs.c degree final project
Utilizing AI and machine learning, the project extracts text from images via Apple's Vision Framework and offers instant answers to questions in documents through the BERT model.
Exploring Python-based projects
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