-hr-matching-tools-

-irish-lyrics-

android-mobile-app

apache-storm

Apache Storm presentation (Team presentation)

azure-speech-texttospeech

Written in Python using the Azure Speech SDK. App.py provides an easy way to create an Text-To-Speech request to Azure Speech and download the wav file. Azure Neural Voices Text-To-Speech enables fluid, natural-sounding text to speech that matches the patterns and intonation of human voices.

bac-game

it's a brain game

classification-projects

Solved Classification Problems

detection-of-semantic-errors-in-arabic-text

An NLP project to detect semantic errors of typographical origin in arabic texts with AraVec

detection-of-semantic-errors-of-typographical-origin-in-arabic-texts

dfjs_scheduling_problem

Destributed Flexible Job Shop Problem solved with GA & Python

fake-news-prediction-using-lstm

generate-text

github-readme-linkedin

📋 A serverless application to get dynamically generated images from your LinkedIn profile on your GitHub READMEs

gru-lstm-conv1d-on-imdb-dataset

implementation-of-an-application-to-classify-relations-according-to-the-context

This project is based on Deep Learning and NLP (Team Project)

intelligent-chatbot-with-llm-

Developement of an interactive ChatBot system by fine-tuning the Llama 13B model on the alpaca-farm dataset.

jobzilla_ai

AI models for automatic job application pipeline (user CV, job description analysis (customized NER/SpaCy) and artificial cover letter generation (trained GPT-2 model) created for Jobzilla project within TechLabs Berlin AI Track programm (03.2021-07.2021).

neat-game-with-python

The world famous NEAT game created along with python thanks to Code Bucket !

news-classification-task

Using DistilBERT model to classify news

pca-algo

predicting-the-next-word

rain-prediction-using-sgd-classifier

Predicting if it's going to rain tomorrow or not using SGDClassifier

recommendation-system

An overview of different Recommendation projects self explored

semantic-textual-similarity

Abstract Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications of this task include machine translation, summarization, text generation, question answering, short answer grading, semantic search, dialogue and conversational systems. We developed Support Vector Regression model with various features including the similarity scores calculated using alignment-based methods and semantic composition based methods. We have also trained sentence semantic representations with BiLSTM and Convolutional Neural Networks (CNN). The correlations between our system output the human ratings were above 0.8 in the test dataset. Introduction The goal of this task is to measure semantic textual similarity between a given pair of sentences (what they mean rather than whether they look similar syntactically). While making such an assessment is trivial for humans, constructing algorithms and computational models that mimic human level performance represents a difficult and deep natural language understanding (NLU) problem. Example 1: English: Birdie is washing itself in the water basin. English Paraphrase: The bird is bathing in the sink. Similarity Score: 5 ( The two sentences are completely equivalent, as they mean the same thing.) Example 2: English: The young lady enjoys listening to the guitar. English Paraphrase: The woman is playing the violin. Similarity Score: 1 ( The two sentences are not equivalent, but are on the same topic. ) Semantic Textual Similarity (STS) measures the degree of equivalence in the underlying semantics of paired snippets of text. STS differs from both textual entailment and paraphrase detection in that it captures gradations of meaning overlap rather than making binary classifications of particular relationships. While semantic relatedness expresses a graded semantic relationship as well, it is non-specific about the nature of the relationship with contradictory material still being a candidate for a high score (e.g., “night” and “day” are highly related but not particularly similar). The task involves producing real-valued similarity scores for sentence pairs. Performance is measured by the Pearson correlation of machine scores with human judgments.

syrineb11

Config files for my GitHub profile.

text-to-speech-using-microsoft-azure-api

training-a-sarcasm-detection-model-using-bidirectional-lstms

transfer-learning-for-nlp-with-tensorflow-hub

travelling-salesman-problem-solved-with-a-dfs-and-bfs-algorithms-

tweet-emotion-recognition-with-tensorflow

create a recurrent neural network and train it on a tweet emotion dataset to learn to recognize emotions in tweets. The dataset has thousands of tweets each classified in one of 6 emotions. This is a multi class classification problem in the natural language processing domain. using TensorFlow as the machine learning framework.