Udacity - Deep Learning Nanodegree Foundation
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README.md
dlnd_image_classification.html
dlnd_image_classification.ipynb
helper.py
problem_unittests.py

README.md

Udacity Deep Laerning Foundations Nanodegree

Project 2: Image classification

In this project, I will classify images of the set CIFAR-10 link. This set contains images of airplanes, dogs, cats and other objects. The data needs to be preprocessed, then a convolutional neural network must be trained. It is necessary to normalize the images, make the one-hot encoding of the labels, and assemble the convolutional layers, max pooling and fully connected . At the end, the classifications proposed by the neural network for the images will be presented.

Running using conda!

Run this in command line

Step 1: Create a new environment

conda create --name image-classification python=3

Step 2: Use the new env

source activate image-classification

Step 3: Install dependencies

conda install -c conda-forge tensorflow=1.0.0
conda install -c conda-forge tqdm=4.11.2
conda install matplotlib scikit-learn jupyter notebook

Step 4: Open the notebook to run it

jupyter notebook dlnd_image_classification.ipynb

Project structure

This folder contains files for Udacity Deep Laerning Foundations Nanodegree Project 2: Image Classification.

dlnd_image_classification.ipynb - Main project file.

dlnd_image_classification.html - Neural network prediction results file.

problem_unittests.py - Unit tests provided by Udacity.

helper.py - Help functions provided by Udacity.