Welcome to Image Understanding with TensorFlow on GCP
In this introductory module you will learn about the rapid growth in high-resolution image data available and the types of applications that it can be applied to. We’ll also cover image data as inputs to your model.
Linear and DNN Models
We’ll start with a brief introduction where we’ll cover the image dataset you will be using for part of this course. Then we’ll tackle an image classification problem with a linear model in TensorFlow. After that we’ll move onto tackling the same problem using a Deep Neural Network. Lastly, we’ll close with a discussion and application of dropout which is a regularization technique for neural networks to help prevent them from memorizing our training dataset.
Convolutional Neural Networks (CNNs)
This module will introduce Convolutional Neural Networks or CNNs for short, and get you started with implementing CNNs using TensorFlow. Since 2012, CNN based systems achieved unparalleled performance on tasks like image recognition and even at playing the ancient board game of Go against the top human champions.
Dealing with Data Scarcity
In this module, we’ll focus on data scarcity, what it is, why it’s important, and, before moving onto building ML models, what you need to do about it
Going Deeper Faster
In this module, you will learn how to train deeper, more accurate networks and do such training faster.You will learn about common problems that arise when training deeper networks, and how researchers have been able to address these issues.
Pre-built ML Models for Image Classification
Welcome to the last module of the Image Classification course. Now that you have build your own image classifiers using linear, DNN, and CNN models with TensorFlow, it’s time to experiment with pre-built image models. In most cases, you will want to try these before investing your time in developing custom TensorFlow code for a model
In this final module, we will review the core concepts covered in this image classification course. You will recall creating classifiers with linear models, DNNs, DNNs with Dropout, Convolutional Neural Networks (CNNs), and lastly with pre-built models like the Cloud Vision API and AutoML Vision.