Welcome to this 2 hour long guided project on creating and training an Object Localization model with TensorFlow. In this guided project, we are going to use TensorFlow's Keras API to create a convolutional neural network which will be trained to classify as well as localize emojis in images. Localization, in this context, means the position of the emojis in the images. This means that the network will have one input and two outputs.

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Think of this task as a simpler version of Object Detection. In Object Detection, we might have multiple objects in the input images, and an object detection model predicts the classes as well as bounding boxes for all of those objects. In Object Localization, we are working with the assumption that there is just one object in any given image, and our CNN model will classify and localize that object.

Please note that you will need prior programming experience in Python. You will also need familiarity with TensorFlow. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, Convolutional Neural Networks, and optimization algorithms like Gradient Descent but want to understand how to use use TensorFlow to solve computer vision tasks like Object Localization.

In this Free Guided Project, you will:

- Create synthetic data for model training

- Create and train a multi output neural network to perform object localization

- Create custom metrics and calbacks in Keras

- Showcase this hands-on experience in an interview

Learn step-by-step:

- Introduction

- Download and Visualize Data

- Create Examples

- Plot Bouding Boxes

- Data Generator

- Model

- Custom Metric: IoU

- Compile the Model

- Custom Callback

- Model Training