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john-fante/README.md

Hi there 👋

Hi, I am Ekin from Istanbul. I am a Machine Learning Engineer (actually I have a bachelor's degree in Electronics and Communication Engineering). In general, my research topics are MLOps/AIOps and Evolutionary Algorithms (genetic algorithm, artificial bee colony algorithm etc.).

I have been interested in image processing since 2020, and machine learning since 2021. It is my code portfolio.

TensorFlow scikit-learn mlflow flask

Python C++

React TailwindCSS WordPress


Table of contents

GitHub Streak

Some of my projects

1. Machine Learning Projects

  • Gamma/Hadron Separation w/XGBoost, LightGBM, SVM (ROC AUC Score: 0.89)
  • Bears vs Pandas SVM, XGB, LGBM, Ensemble Method w/Noised-Dataset (ACC: 96.75 %)
  • Groundwater Quality Classification w/CatBoost
  • (QSAR) Prediction Biological Activity w/CatBoost Implementation (MCC: 0.825)
  • (QSAR) Classification Activity of Inhibitors of EphA4 Receptor Antagonists (AID 689) w/CatBoost (MCC: 0.782)
  • Alive/Dead Diabetic Outcome Prediction with CatBoostClassifier w/CatBoost (ROC AUC Score: 0.77)
  • Pistachio Classification w/CatBoost (ROC AUC Score: 0.9)
  • Rain Prediction w/CatBoost (F1-Score:0.84)

    • 2. Deep Learning Projects

    2.1 Classification

  • Endoscopy Image Classification w/Swin Transformer (F1 Score: 0.87)
  • Crop Disease Classification w/Feature Fusion (EfficientNet, MobileNet) (F1 Score: 0.8)
  • 30 Plants Detection w/Custom ConvMixer based CNN (F1 Score: 0.77)
  • Bladder Tissue Classification w/ViT (F1 Score: 0.82)
  • Blood Cells Classification w/Custom ConvMixer based CNN (F1 Score: 0.98)
  • Brain Tumor Classification (Normal, Glioma, Meningioma, Pituitary) (Test ACC: 86.27 %)
  • Car Model and Color Multiclass Classification (F1 Score: 0.74)
  • Dental X-Rays Classification w/TPU (F1 Score: 0.72)
  • The Fashion Mnist Distributed DL Example (Mirrored Strategy) (Test ACC: 88.35 %)
  • Down Syndrome Detection w/CNN (Test AUC Score: 0.87)
  • Earthquake Events Classification (Mojor Event/Non-Major Event) (Test ACC: 64 %)
  • Fungus Detection w/10 Kfold CV Custom ConvMixer (F1 Score: 0.85)
  • Hieroglyph Multiclass Classification DenseNet (F1 Score: 0.85)
  • Higgs/Background Process Classification w/CNN using TPU (AUC Score: 0.83)
  • Jellyfish Classification (10KFold CV w/Custom ConvMixer) (F1 Score: 0.87)
  • MRI Sequence (T1, T2, T1 C+) Classification w/Custom CNN
  • Normal heartbeat/Myocardial Infarction Classification (ROC AUC Score: 0.842)
  • Pneumonia Detection w/Ensemble DL (Test AUC Score: 0.91)
  • Zipper Defect Classification (AUC Score: 0.98)
  • Glaucoma Classification w/ViT (F1 Score: 0.91)
  • Chest X-Ray Classification w/ViT (F1 Score: 0.9)
  • Document Classification w/ViT
  • (76 GB) 160 Polish Bird Sounds Classification
  • Rice Classification w/Custom ResNet50 (ACC 85%)
  • Maize Classification w/Custom ResNet18 (AUC Score: 0.98)
  • Sport Scene Classification w/ViT (3 KFold CV)

    • 2.2 Segmentation

  • Brain tissue segmentation with U-net using TPU (Val Dice Coef: 0.88)
  • Brain tumor/anomaly segmentation with U-net using TPU
  • Asphalt Pavement Crack Segmentation U-Net
  • Eye Conjunctiva Segmentation with U-Net using TPU (Dice : 0.91, Jaccard : 0.82)
  • Iris Segmentation U-net w/TPU (Dice : 0.94, Jaccard : 0.88)
  • Particle Segmentation Custom DeepLabv3+ (Dice : 0.93, Jaccard : 0.88)
  • Retina Vessel Segmentation U-net w/TPU (Dice : 0.75)
  • Lung Segmentation UNet w/SeparableConv (Dice : 0.93)
  • Segmentation Medical Instrument w/Custom DeepLabv3+ (Dice : 0.86)
  • Tomato Segmentation w/detectron2 (mask AP: 61.94)
  • Brain Tumor Segmentation w/detectron2 (mAP@50:76.2)
  • Road Segmentation w/DeepLabv3+ from Scratch
  • Segmenting HuTu Cells DeepLabv3+ (Test Dice: 0.93)

    • 2.3 Object Detection

  • Damaged Lamp Detection w/detectron2 (Faster R-CNN)
  • Plate Detection w/detectron2 (mAP@75: 89.19)
  • Tomato Detection w/detectron2 (mAP@50: 82.02)
  • Tiny Vehicle Detection w/detectron2 (mAP@50: 32.08)
  • Traffic Signs Detect w/detectron2 (mAP@50: 71.62)
  • Sign Detection w/Keras YOLO V8
  • Road Mark Detection (ResNet-50, ResNeXt 101 FPNs)
  • Bone Fracture Detection (ResNet-50, ResNeXt 101 FPNs)
  • Acne Detection w/Keras YOLO V8
  • Brain Tumor Detection w/Keras YOLO V8

    • 2.4 Natural Language Processing

  • PaliGemma 3B for License Plate OCR
  • arXiv Articles Multi Label Classification w/FNet
  • IEEE Research Papers Topic Modelling w/LDA
  • Depressive vs Non-depressive Tweet w/Custom FNet (F1 Score: 0.88)
  • Yelp Review Stars Prediction (Classification) w/Gemma 7B (LoRA)
  • BBC News Topic Modeling w/LDA
  • Manufacturing Question-Answer w/Gemma 7B (Fine-Tuning LoRA) (Cosine Sim: 0.83)
  • Graph to Table w/Google's DePlot Model
  • Disease Article Topic Modelling w/BERTopic
  • Spam vs Ham Message w/ Gemma 7B Fine-Tuning (LoRA)
  • News Zero-Shot Topic Modelling w/BERTopic
  • Social Media Post Multiclass Classification w/DistilBERT
  • Gemma 2B Text Summarization w/Zero-Shot Prompting
  • Rating Prediction w/SentenceTransformer, CatBoost (MAE: 0.381)
  • Spam vs Ham Message Classification w/Custom FNet (F1 Score: 0.92)
  • Sentiment Analysis w/CatBoostClassifier (F1 Score: 0.703)
  • Complaint Analysis w/Ensemble Model (CatBoost, LR) (F1 Score: 0.86)
  • News Analysis w/Tensorflow (DistilBERT fine-tuning) (F1 Score: 0.89)
  • Emotion Classification w/LogisticRegression
  • Spam Mail Detection w/Tensorflow (DistilBERT fine-tuning) (F1 Score: 0.92)

    • 2.5 Other Deep Learning Projects (Anomaly Detection, Image Captioning, Multiple Instance Learning etc.)

  • PaliGemma 3B for Image Captioning
  • Ford Motor Data Anomaly Detection with AutoEncoder
  • Satellite Image Captioning (ViT, Bi-LSTMs)
  • Molecule Activity, Deep Multiple Instance Learning
  • Cloud Classification (Involution Neural Network)
  • Car Detect w/Deep Multiple Instance Learning
  • Happy Detection w/Deep Multiple Instance Learning

    • 3. Hybrid Model (Deep Learning and Machine Learning) Projects

  • (QSAR) Renin Activity (ChEMBL286) Classification w/Ensemble Model(CNN + CatBoost) (F1-Score: 0.84)
  • Leaf Disease Detection w/Hybrid Model (ViT, PCA, SVM) (F1 Score: 0.92)
  • Flower Detection w/Hybrid Model (ViT, CatBoost, SHAP) (F1 Score: 0.96)
  • Skin Cancer Detection w/Hybrid Model (ConvMixer, CatBoost, SHAP)
  • Smoking Detection w/Hybrid Model (ViT, XGBoost, SHAP) (F1 Score: 0.96)
  • Disease Severity Hybrid Classifier (ViT,CatBoost) (F1 Score: 0.75)
  • Diamond Detect w/Hybrid Model (ViT,CatBoost,SHAP) (F1 Score: 0.97)
  • Mammals Classification w/Ensemble Deep Learning (F1 Score: 0.92)

    • 4. Online/Incremental Learning Projects

  • Cryptocurrency (AVAX) price prediction with Incremental/Online Learning
  • Smoking Image Detection w/Online Learning (River) (F1 Score: 0.95)
  • Turbine Power Output Forecasting w/Online Learning (River)
  • Tesla Stock Price Prediction w/Online Learning

    • 5. Machine Learning Theory

  • Simple New Sample Generation from MNIST w/KDE
  • Simple New Sample Generation FashionMNIST w/KDE
  • Minkowski vs Hassanat Distance Metric Implementation w/KNN
  • Proof of the Entropy of The Gaussian Distribution Implementation
  • Basic Decision Tree Project and the ccp_alpha parameter tuning (Coursera Project Network)

    • 6. Algorithmic Trading

  • Getting Binance Current Coins Prices Volumes

    • Competitions


    My degree Type of Competition My solution algorithm Link
    Forecasting Mini-Course Sales 253/1172 Time Series Forecasting Deep Learning https://www.kaggle.com/competitions/playground-series-s3e19
    Predict CO2 Emissions in Rwanda 256/1440 Regression CatBoost, LightGBM https://www.kaggle.com/competitions/playground-series-s3e20
    Petals to the Metal - Flower Classification on TPU 40/118 Classification Ensemble Deep Learning https://www.kaggle.com/competitions/tpu-getting-started

    Popular repositories Loading

    1. down-syndrome-detection down-syndrome-detection Public

      Down Syndrome Detection with CNN

      Jupyter Notebook 2

    2. my-deep-learning-projects my-deep-learning-projects Public

      My Deep Learning Projects (Classification, Segmentation, Object Detection, NLP, Deep Multiple Instance Learning)

      Jupyter Notebook 2

    3. crack-segmentation crack-segmentation Public

      SUT-Crack segmentation U-Net

      Jupyter Notebook 1 1

    4. my-hybrid-model-projects my-hybrid-model-projects Public

      My Hybrid Model (Deep Learning and Machine Learning) Projects

      Jupyter Notebook 1

    5. sahibinden-fotograf-indirme sahibinden-fotograf-indirme Public

      Sahibinden üzerinden herhangi bir ilandan, kategoriden veya özelleştirilmiş bir aramadaki tüm ilanların görsellerini indirmeye yarar.

      Python

    6. john-fante john-fante Public

      In my code portfolio, I generally try new techniques and methods in machine learning. I don't like only copying and pasting.