Convolutional Neural Networks in TensorFlow

If you are a software developer who wants to build scalable AI algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning with Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source machine learning framework. In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques for improving the computer vision model you built in Course 1. You will learn how to work with real-world images of different shapes and sizes, visualize an image’s journey through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including padding and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models. Andrew Ng's Machine Learning course and Deep Learning Specialization teach the most important and fundamental principles of machine learning and deep learning. The new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement these principles, so you can start building and applying scalable models to real-world problems. For a deeper understanding of how neural networks work, we recommend the Deep Learning Specialization.