felipelodur/ConvNet-CIFAR-10
Project developed as coursework for Udacity "Deep Learning Fundamentals" Nanodegree
This project helps deep learning students and practitioners classify images into predefined categories. You input a dataset of small images (like the CIFAR-10 dataset containing objects such as airplanes, dogs, and cats) and it outputs a trained convolutional neural network capable of predicting the category of new, unseen images. This is designed for individuals learning or applying fundamental deep learning concepts, specifically image classification.
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Use this if you are a deep learning student or beginner looking for a foundational example of how to implement a convolutional neural network for image classification.
Not ideal if you need a high-performance, production-ready image classifier or a model for very large, complex datasets beyond basic examples.
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Jan 24, 2018
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