Description

Modernize ASP.NET Web Apps with Azure App Service

Modernize ASP.NET Web Apps with Azure App Service
.MP4, AVC, 380 kbps, 1920×10800 | English, AAC, 160 kbps, 2 Ch | 3h 46m | 829 MB Instructor: Marius Zaharia

Modernize and transform your application to microservice, serverless, and container architectures with Azure App Service

Do you want to transform and modernize your legacy applications for the cloud? Azure App Service lets you quickly build, refactor, deploy, and scale enterprise-grade web apps running on any platform, without having to manage the infrastructure. You will take a typical legacy ASP.NET web application and transform it into a modern application with ASP.NET Core and Azure App Service as the main drivers of the change.

By the end of this course, you will have the skills to use Azure App Service to refactor, modernize, deploy, and highly scale your legacy applications.

3. EXCITING PROJECTS

Are you tired of courses based on over-used, outdated data sets?

Yes? Well then you’re in for a treat.

Inside this class we will work on Real-World datasets, to solve Real-World business problems. (Definitely not the boring iris or digit classification datasets that we see in every course). In this course we will solve six real-world challenges:

• Artificial Neural Networks to solve a Customer Churn problem
• Convolutional Neural Networks for Image Recognition
• Recurrent Neural Networks to predict Stock Prices
• Self-Organizing Maps to investigate Fraud
• Boltzmann Machines to create a Recomender System
• Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize

*Stacked Autoencoders is a brand new technique in Deep Learning which didn’t even exist a couple of years ago. We haven’t seen this method explained anywhere else in sufficient depth.

4. HANDS-ON CODING 

In Deep Learning A-Z™ we code together with you. Every practical tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means.

In addition, we will purposefully structure the code in such a way so that you can download it and apply it in your own projects. Moreover, we explain step-by-step where and how to modify the code to insert YOUR dataset, to tailor the algorithm to your needs, to get the output that you are after.