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Building and Deploying Keras Models in a Multi-cloud Environment

      Author: VNC   |   09 July 2018   |   comments: 0

Building and Deploying Keras Models in a Multi-cloud Environment

Building and Deploying Keras Models in a Multi-cloud Environment
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3 Hours | 598 MB
Genre: eLearning | Language: English

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.

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.

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