Deep Learning and Google Cloud
Course Description
In this course we will learn about the basics of deep neural networks, and their applications to various tasks. The course aim is to present the mathematical, statistical, and computational challenges of building stable representations for high-dimensional data. This course teaches the important concepts and terminology for working with Google Cloud. The courses explain how the cloud computing evolved and how the unique features of Google’s approach to it.
Course Objectives:
| · | Learn the tools required for building Deep Learning models |
| · | Explore multiple architectures and understand how to fine-tune and continuously improve models |
| · | Learn how the same task can be solved using multiple Deep Learning approaches |
| · | Learn and compare many of the computing and storage services available in Google Cloud Platform |
| · | Learn about important resource and policy management tools used |
| · | Learn the skills needed to be successful in a cloud architecture role |
Course Contents:
| · | Shallow and Deep Neural Networks |
| · | Explaining and Harnessing Adversarial Examples |
| · | Optimization |
| · | Gradient Checking |
| · | Hyperparameter tuning |
| · | Programming Frameworks |
| · | Conditional GAN, CycleGAN |
| · | Introduction to Google Cloud Platform |
| · | Storage and Containers in Cloud |
| · | Virtual Machines |
| · | Applications |
| · | Big Data and Machine Learning |
| · | Developing and Monitoring |
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