a_star_taquin

alpaca-lora

Instruct-tune LLaMA on consumer hardware

angular_gl4_rainbow

applied-ml

📚 Papers and blogs by organizations sharing their work on data science & machine learning in production.

automl

Google Brain AutoML

azayz

My portfolio repo

data-colab-engineering-test

Data-Colab is a Tunisian NGO that aims to upgrade and help AI community in Tunisia, this is my work on their test to join their engineering department fortunately i was accepted. The test consists of 3 parts the first part is about computer vision and transfer learning the second part is about NLP and the third part is about general datascience and artificial intelligence.

django_training

eda-ml-global-terrorism-database

My work on the global terrorism database in Kaggle, my goal was to explore different machine learning algorithms, their validations and test out diffeent scoring metrics and perform EDA o n the Dataset.

ept-hackathon-ai

My work during EPT's mini AI hackathon the goal was to create a regression model to help hotel pricing decision making using GridSearchCV along woth CatBoostRegressor I managed to get 4'th place. The data can be found on kaggle : https://www.kaggle.com/c/ai-mini-hackathon-ept/data

facial-emotion-recognition

I wanted to create my own FER program that works real time to further expand my knowledge on CNN, Data augmentation and Transfer Learning. Data is used from a kaggle competition, models architectures are purely my own using Keras I have achieved 64% accuracy which I think is decent but there's space for improvement. I didn't work on face detection myself but my future goals are to implement face detection myself, work on real time recognition and Improve model's performance.

fastapi_training

This repo contains my code for learrning FastAPI, CRUD, Async operations

financial-inclusion-in-africa-competition

This is my work for AI HACK qualification, my goal was to explore as many classification models as i can, i tried some feature engineering techniques and modified multiple featues. The models I used are KNN, Random Forest, Decision Tree, MLP, AdaBoost, XGBoost I used ROC/AUC to compare between models and accuracy aswell finally I chose the best models and applied Stacking to them which gave me the best result. I explored aswell other techniques such as PCA, LDA and SMOTE because the data was unbalanced, I also built a small NN using Keras. The data can be found on Zindi : https://zindi.africa/competitions/financial-inclusion-in-africa/data

flan-distributed

Distributing Flan Model using Jina Ecosystem

it-cert-automation-practice

Google IT Automation with Python Professional Certificate - Practice files

jina

Cloud-native neural search framework for 𝙖𝙣𝙮 kind of data

keras-focal-loss

Implementation of binary and categorical/multiclass focal loss using Keras with TensorFlow backend

llm-paper-summary-retrieval

models

Models and examples built with TensorFlow

nest_js_crud_todo_app

objectron

Objectron is a dataset of short, object-centric video clips. In addition, the videos also contain AR session metadata including camera poses, sparse point-clouds and planes. In each video, the camera moves around and above the object and captures it from different views. Each object is annotated with a 3D bounding box. The 3D bounding box describes the object’s position, orientation, and dimensions. The dataset contains about 15K annotated video clips and 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes

peft

🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.

pentbox-python

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

tunisian-arabic-dialect-sentiment-analysis

Sentiment Analysis task on Tunisian and Arabic dialect, data augmentation for NLP and scrapping google maps for more data

tweetanalyserdisasterornot-nlp

This is my work on the kaggle comeptition Disaster or Not. It's about classifying tweets : tweets that are reporting actual and real disasters and tweets that aren't. In my work I used a lot of NLP techniques word2vec / TF-IDF / lemmitazation / Words clouds. In modeling I applied ensembling and boosting models to get best Results. Next up I ll try embeddings and BERT.