Convolutional Neural Network(CNN) project for Udacity's Machine Learning Engineer Nanodegree. I learned how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, my algorithm identifies the dog's breed (specifying it's confidence). If supplied an image of a human, the code identifies the resembling dog breed. If neither a human nor a dog are provided as input, the app recognizes it ignorance about the non-human & non-Dog Realm.

• First I used VGG16 to learn to train a model using Transfer learning.
• For the final model I used VGG19's bottleneck features (Output after convolutional layers) as input to my own architecture.

VGG16 & VGG19

My Architecture

• Project Notebook
• Project Review

• Intro
• Step 0: Import Datasets
• Step 1: Detect Humans
• Step 2: Detect Dog
• Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
• Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)
• Step 5: Write Your Algorithm
• Step 6: Test Your Algorithm