Udemy

The Theory of Deep Learning - Deep Neural Networks 2026

Enroll Now
  • 791 Students
  • Updated 12/2025
4.1
(40 Ratings)
CTgoodjobs selects quality courses to enhance professionals' competitiveness. By purchasing courses through links on our site, we may receive an affiliate commission.

Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
0 Hour(s) 34 Minute(s)
Language
English
Taught by
The Click Reader, Merishna Singh Suwal
Rating
4.1
(40 Ratings)

Course Overview

The Theory of Deep Learning - Deep Neural Networks 2026

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

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 2026 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.

Course Content

  • 4 section(s)
  • 9 lecture(s)
  • Section 1 Introduction
  • Section 2 Neurons
  • Section 3 Deep Neural Network
  • Section 4 End of the Course

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.

Skills covered in this course


Reviews

  • M
    Moro Afriyie
    4.5

    I love this course and i'm glad it helped me understand it

  • A
    Aminur Rahman Ashik
    3.5

    Good to review my knowledge I have through this course

  • N
    Nikhil Bhargava
    5.0

    gr8

  • S
    Supreet Bose
    5.0

    Great for basics.

Start FollowingSee all

We use cookies to enhance your experience on our website. Please read and confirm your agreement to our Privacy Policy and Terms and Conditions before continue to browse our website.

Read and Agreed