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The Theory of Deep Learning - Deep Neural Networks 2022

The Theory of Deep Learning - Deep Neural Networks 2022

Learn the theory behind how Deep Neural Networks work through mathematical as well as real-life examples.

What you'll learn

  • Know about the inspiration behind Deep Learning.
  • Learn about neurons and how they compute.
  • Gain fundamental knowledge of activation functions.
  • Learn how Gradient Descent is performed for error minimization.
  • Learn about the feed-forward and back-propagation cycles of Deep Neural Networks.

Requirements

Description

Learn The Theory of Deep Learning in the most comprehensive and up-to-date course on the topic created by The Click Reader.

In this course, you will learn the inspiration behind deep learning and how it relates to the human brain. You will also gain clear knowledge about the building blocks of neural networks (called neurons) along with how they compute, make predictions, and learn.

We will then move on to learning the theory of deep neural networks, including how data is fed into it, how neurons compute the data, and how predictions are made. We'll end the course by learning how deep neural networks learn/train using a combination of feed-forward and back-propagation cycles.

Also, do not worry if you're not great at mathematics since we've covered all the necessary mathematical concepts in the course itself along with real-life examples.

After going through this course, you will gain all the know-how of how to build your own deep neural network from scratch.

Why you should take this course?

  • Updated 2022 course content: All our course content is updated as per the latest technologies and tools available in the market
  • Guided support: We are always there to guide you through the Q/As so feel free to ask us your queries.

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