Image Classifier Model - Deep Learning model to recognize different species of flowers.

Table of Contents

1. Installation
2. Project Motivation
3. Results
4. Licensing, Authors, and Acknowledgements

Installation

There should be no necessary libraries to run the code here beyond the Anaconda distribution of Python. The code should run with no issues using Python versions 3.*.

Project Motivation

In this project, you will implement an image classification application using a deep learning model on a dataset of images. You will then use the trained model to classify new images. First you will develop your code in a Jupyter notebook, then convert it into a Python application that you will run from the command line of your system.

1. IMPLEMENTING GRADIENT DESCENT: Implement gradient descent to train deep learning networks.
2. TRAINING NEURAL NETWORKS: Learn about techniques for how to improve training of a neural network, such as: early stopping, regularization, and dropout.
3. KERAS: Learn how to use Keras for building deep learning models
4. DEEP LEARNING WITH PYTORCH: Learn how to use PyTorch for building deep learning models

Results

In this project, I have implemented an image classification application using a deep learning model on a dataset of images. First I have trained the model to classify new images using Jupyter notebook and then converted it into a Python application that will run from the command line in a system. A Udacity Data Scientist Nanodegree Project-Term1.

Licensing, Authors, Acknowledgements

Feel free to use the code here as you would like! Thanks to Udacity for all the support.