Deep Learning Architecture for Building Artificial Neural Networks [Video]

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Deep Learning Architecture for Building Artificial Neural Networks [Video]

Anshul Srivastav
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An introduction to deep learning and its architectures with real-world use cases
This title is available to pre-order now and is expected to be published in
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$10.00
RRP $124.99
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Video Details

ISBN 139781788395106
Course Length2 hours

Video Description

The course starts off with an introduction to Deep Learning and the different tools, hardware, and software before we begin to understand the different training models. We then get to what everyone is talking about: Neural Networks. Here we understand how Neural Networks work and the benefits they offer for supervised and well as unsupervised learning before building our very own neural network. We will then move on to understanding the different Deep Learning Architectures, including how to set up your architecture and align the output. Finally we take a look at Artificial Neural Networks and understand how to build your own ANN.

Taking this course will help you dive head first into the popular field of deep learning as a career choice or for further learning.

Style and Approach

This course will give you an introduction to deep learning and its architectures with real-world use cases and neural networks, while exposing you to Deep Learning architectures. Also covered will be an introduction to Artificial Neural Networks and their implementation with practical sessions.

Table of Contents

What You Will Learn

  • Learn how to utilize deep learning models and build business models around them
  • Gain a practical understanding of the architectures required to develop business use cases
  • Align your data science strategy with current and future systems in their respective ecosystem
  • Start to define data science problems and align them to specific business use case
  • Conceptualize process improvements with Deep Learning
  • Visualize leakages in your organization and fix them with deep learning
  • Predict outcomes of processes and propose improvements

Authors

Table of Contents

Video Details

ISBN 139781788395106
Course Length2 hours
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