This project used CNN architecture to create a multi-class image classification model for a Kaggle competition with tomato images. My architecture achieved 100% accuracy.

In this project we're going to be using machine learning to help us identify different breeds of dogs. To do this, we'll be using data from the Kaggle dog breed identification competition. It consists of a collection of 10,000+ labelled images of 120 different dog breeds. This kind of problem is called multi-class image classification. It's multi-class because we're trying to classify mutliple different breeds of dog. If we were only trying to classify dogs versus cats, it would be called binary classification (one thing versus another). Multi-class image classification is an important problem because it's the same kind of technology Tesla uses in their self-driving cars or Airbnb uses in atuomatically adding information to their listings. Since the most important step in a deep learng problem is getting the data ready (turning it into numbers), that's what we're going to start with.

Python scripts and materials for Kaggle's Leaf Classification Competition. Tools: Pandas, subprocess, Keras Neural Networks, Multi-Class Classification, Image Processing

Machine Learning Engineer Capstone Project. Multi-Class Deep Learning Classification (CNN) to classify images in the Amazon Rainforest. Kaggle Competition.

Multi-class image classification model for Rice, Pistachio, and Grapevine leaves developed for PALS TurboTech Build Hackathon 2025 (Kaggle Competition).

Deep learning project for a Kaggle competition for IFT 6390, implementing MLP from scratch (NumPy) and CNNs (PyTorch/TensorFlow) for multi-class image classification.

Using Mobile-Net's deep-learning model and transfer learning, I developed a multi-class classification model that can take an image of a dog and predict its breed. This project was inspired from a Kaggle challenge (https://www.kaggle.com/competitions/dog-breed-identification/)

This project uses TensorFlow and Deep Learning to identify different breeds of dogs. The data is sourced from the Kaggle dog breed identification competition. It consists of a collection of 10,000+ labelled images of 120 different dog breeds. This is a multi-class image classification because there are multiple dog breeds (classes).

In this project machine learning is used to identify different breeds of dogs. To do this, data from the Kaggle dog breed identification competition is used. It consists of a collection of 10,000+ labelled images of 120 different dog breeds. This kind of problem is a multi-class image classification problem. It's multi-class because we're trying to classify mutliple different breeds of dog. If we were only trying to classify dogs versus cats, it would be called binary classification (one thing versus another).