john-fante | coder

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.

Table of contents

Machine Learning (projects used scikit-learn, XGBoost, Catboost etc.)
Deep Learning Classification (projects used Tensorflow with CNNs and ViTs models)
Deep Learning Segmentation (UNet, DeepLabv3+, detectron2 etc. models)
Deep Learning Object Detection (detectron2, YOLO etc. models)
Natural Language Processing (text classification, topic modelling, prompting etc.)
Others DL project (multiple instance learning, image captioning etc.)
Hybrid Models (projects used a deep feature extractor and an ML classifier)
Online/Incremental Learning (projects used online learning methods and libraries like River)
Theoretical Machine Learning (some proofs of machine learning theory with python scripts)
Custom Tensorflow Callbacks (for example, a callback creating a test set classification report during training)
React Web App Projects (my React, Vite and Tailwind CSS projects)

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

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.)

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

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

john-fante Goto Github PK

Hi there 👋

Some of my projects

1. Machine Learning Projects

2. Deep Learning Projects

2.1 Classification

2.2 Segmentation

2.3 Object Detection

2.4 Natural Language Processing

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

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

4. Online/Incremental Learning Projects

5. Machine Learning Theory

6. Algorithmic Trading

Competitions

Arturo Bandini Jr. - Ekin's Projects

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