What you'll learn
Inspiration behind deep learning.
Neurons and how they compute.
Fundamental knowledge of activation functions.
Gradient descent for error minimization.
Feed-forward and Back-propagation of Deep Neural Networks.
Requirements
Basic knowledge of Machine Learning
Description
Learn The Theory of Deep Learning in this detail course 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 a clear knowledge about the building blocks of deep learning (called neurons) along with how they compute, make predictions and learn.
We will then move onto 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.
Who this course is for:
Any beginner, intermediate, or expert developer looking to work on deep learning.

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