Matlab neural networks (laboratory projects).
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Updated
Apr 10, 2018 - MATLAB
Matlab neural networks (laboratory projects).
Build a Python application that can train an image classifier on a dataset, then predict new images using the trained model.
Capstone Project for Udacity Machine Learning Engineer NanoDegree
Using Machine Learning Models to classify images of CIFAR10
A microservice that uses keras and tensorflow to classify images
Image Classifier using common DL Frameworks
Project 2 of Udacity's Introduction to Machine Learning Nanodegree Program
Flowers Image Classifier that can classify 102 different types of flowers from their images using transfer learning.
Utilized CNN models to classify images of mountains and forests, treating mountains as the positive class and forests as the negative class. We compare the performance of a pre-trained model, a custom CNN model, and a CNN model with data augmentation.
Image classifier on CIFAR-10 Dataset, using tensorflow
Udacity's project of classifying images by using pytorch and transfer learning
Built image classification deep learning architectures - AlexNet, VGG16, and ResNet using transfer learning and fine-tuning in PyTorch. Final model accuracies achieved are AlexNet-81.2%, VGGNet-85.6%, ResNet-84.7% on 10K test images.
A cat and dog classifier.
This is an image classifier using a convolutional neural network (using keras library, tensorflow backend) in which the dependent variable is binary (can be explained by two classes)
A command line application that implements an image classifier with PyTorch. Part 2 of final project for Udacity's AI Programming with Python Nanodegree program.
Basic image classifier made from scratch using Deep learning techniques.
Detects and classifies Apples, Oranges and Bananas
This repository is for the projects of AI Programming with Python nanodegree of Udacity
Image Classifier built using Python, OpenCV. Using ORB for feature detection and knn matcher for matching the features.
Using computer vision to identify species in wildlife camera trap images to automate the dataset labeling process.
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