Building and Deploying Keras Models in a Multi-cloud Environment

Deep learning is merged into the normal operations of many companies due to the availability of huge repositories of data and easy to develop learning frameworks. Here, you'll use Keras to develop one such network or implement into your own model.
Course info
Level
Beginner
Updated
Jun 29, 2018
Duration
2h 56m
Table of contents
Composing Sequential Models in Keras
Using the Functional API in Keras
Running Keras on Microsoft Azure
Running Keras on Amazon AWS
Running Keras on Google Cloud ML Engine
Course Overview
Description
Course info
Level
Beginner
Updated
Jun 29, 2018
Duration
2h 56m
Description

As machine learning and deep learning techniques become popular, the importance of intuitive and simple abstractions that enable fast development and quick prototyping of these models become critical. In this course, Building and Deploying Keras Models in a Multi-cloud Environment, you'll learn the simple and intuitive functions and classes that Keras offers to build neural network models. First, you'll gain an understanding of the basic working of a neuron and how neural networks are structured and trained. You'll study the simplest form of a model, a network for linear regression which can be built using the simple Sequential model class in Keras, along with other forms of Sequential models such as convolutional neural networks for image classification. Next, you'll move on to recurrent neural networks and understand their ability to store state using outputs from previous time instances, and build a sequence-to-sequence RNN for language translation from English to French using Keras' functional API. Lastly, you'll learn to build and train these models on the most popular cloud platforms, Azure, AWS and the GCP. You'll study their IaaS and PaaS offerings for machine learning and use deep learning VMs or the distributed training framework to train our models. By the end of this course, you will be very comfortable using the Keras high-level API to build your machine learning models and know how you can take these models to the cloud for training at scale.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi! My name is Janani Ravi, and welcome to this course on Building and Deploying Keras Models in a Multi-cloud Environment. A little about myself. I have a Masters in Electrical Engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. In this course, we'll learn the simple and intuitive functions and classes that Keras offers to build neural network models. We start off by understanding the basic working of a neuron and how neural networks are structured and trained. We then study the simplest form of model, a network for linear regression which can be built using the simple sequential model class in Keras. We'll study other forms of sequential models as well, such as convolutional neural networks for image classification. We then move on to recurrent neural networks and understand how they have the ability to store state using outputs from previous time instances. We then build a sequence-to-sequence RNN for language translation from English to French using Keras' functional API. We then see how we can build and train these models on the most popular cloud platforms today--Azure, AWS, and the GCP. We'll study the Infrastructure as a Service and Platform as a Service offerings for machine learning and use deep learning VMs or the distributed training framework to train our models so that you get an idea of how each of these work. At the end of this course, you will be very comfortable using the Keras high-level API to build your neural network, and you'll know how you can take these models to the cloud to train at scale.