Deep Learning Training script implemented in Python based on Deep Learning model T5 --- Teaching Google's T5 how to paraphrase sentences through fine tuning with PAWS dataset.

Smart Sentiment Analysis and Classification of Arabic Tweets by building and merging deep learning models

Deep learning project for SDAIA T5 Data Science Bootcamp. This project is images classification using neural network, convolution neural network, and Transfer learning to detect whether someone is wearing glasses or not.

The project's goal is to use different Deep Learning techniques - T5 Transformer, Encoder & Decoder with BiLSTM models, and NLP to generate coherent summaries – to generate brief descriptions of news stories.

t5,huggingface,transformers,nlp,question-answering english-premier-league,epl,football,soccer python,deep-learning,fine-tuning

We perform a machine-learning guided analysis of ancient religious texts. We find that deep learning models, such as RoBERTa and T5, are able to identify theme and even authorship.

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This is the 5th project for the Data Science T5 Bootcamp. Using Deep Learning algorithms, it is called "predicting traffic for smart cities."

Deep learning project for SDAIA T5 Data Science Bootcamp. This project to build a deep neural network model that uses image data to extract its features and then recognize it.

Named Entity Recognition (NER) project for IT4772E - Natural Language Processing. Implements deep learning models, including encoder-only (BERT, RoBERTa, BERT-CRF) and encoder-decoder (T5, BART). Covers dataset preprocessing, model training, evaluation, and API deployment.

Transformers are deep learning architectures that use self-attention instead of recurrence, enabling parallel sequence processing. Introduced in 2017 (Attention Is All You Need), they capture long-range dependencies effectively. Transformers power modern NLP models like BERT, GPT, T5, and BART for translation, summarization, and text generation.

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Final project - SDAIA Academy - Bootcamp Data Science

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Deep learning project text summarization from BBC News using T5 Model

Automatic Text Summarization using LSTM and T5 Transformer. This project generates concise summaries from long texts using deep learning and Transformer models, comparing traditional Seq2Seq (LSTM) and modern T5-based approaches.

T5 Multimodal Summarizer is a deep learning project that fine-tunes a T5 model to generate summaries from text, PDF documents, and audio. It features an interactive Gradio UI and supports real-time summarization, transcription, and text-to-speech.

Fine tuned T5- small model to extract structured labels from radiologist reports. Used to create labelled dataset for deep learning model training by converting unstructured diagnosis text into five key fields.

Q&Arabic is an NLP framework that generates Arabic FAQs from a given material. The project uses deep learning models (BERT and T5) and includes a detailed report and brief presentation covering the system analysis, related work, and future plans.

Developed a suite of unsupervised and deep learning models including PCA/t-SNE for dimensionality reduction, clustering for automotive risk analysis, LSTM forecasting on agricultural climate-linked time series, and BERT/T5 models for tweet classification and Q&A tasks. Evaluated performance with metrics such as RMSE, R², ROC-AUC, ROUGE,

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CNN project

I will construct three model and use three targets: Age Gender Ethnicity After training models I'm going to create a function which will predict these three parameters by photo.

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This project trains a DQN model to play a variety of Atari Games, including Q*bert. It includes a random agent, which generates gameplay based on the machine making random actions, as well as a trained model that attempts to make desired actions to win the game. Reinforcement learning is an area of machine learning that is focused on training agent

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