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Introduction to Deep Learning & Neural Networks with Keras

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BeginnerCourse

Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.Upon course completion, learners will gain the capacity to describe neural networks and deep learning models, as well as discern the distinctions between them.

4.7 (21k+ Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Industries

  • Information Technology

Enrollment Count

  • 713.63K

Skills You Will Learn

  • Keras, Deep Learning, Machine Learning, Artificial Neural Network, Artificial Intelligence (AI)

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 8 hours

Platform

  • Coursera

Last Update

  • October 12, 2025
About this Course

Course Syllabus


Module 1

  • Introduction to Neural Networks and Deep Learning
In this module, you will learn about exciting applications of deep learning and why now is the perfect time to learn deep learning. You will also learn about neural networks and how most of the deep learning algorithms are inspired by the way our brain functions and the neurons process data. Finally, you will learn about how neural networks feed data forward through the network.

Module 2

  • Artificial Neural Networks

    In this module, you will learn about the gradient descent algorithm and how variables are optimized with respect to a defined function. You will also learn about backpropagation and how neural networks learn and update their weights and biases. Futhermore, you will learn about the vanishing gradient problem. Finally, you will learn about activation functions.

Module 3

  • Keras and Deep Learning Libraries

In this module, you will learn about the different deep learning libraries namely, Keras, PyTorch, and TensorFlow. You will also learn how to build regression and classification models using the Keras library.


Module 4

  • Deep Learning Models

In this module, you will learn about the difference between the shallow and deep neural networks. You will also learn about convolutional networks and how to build them using the Keras library. Finally, you will also learn about recurrent neural networks and autoencoders.

Course Project

In this module, you will conclude the course by working on a final assignment where you will use the Keras library to build a regression model and experiment with the depth and the width of the model.

Instructors

Artem Arutyunov

Data Scientist

Hey, Artem here! I am excited about answering new challenges with data science, machine learning and especially Reinforcement Learning. Love helping people to learn, and learn myself. Studying Math and Stats at University of Toronto, hit me up if you are from there as well.

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Roodra Kanwar

Data Scientist at IBM

I am a data scientist by day, superhero by night. Psych! I wish I was that cool. Only the former part is true which is still pretty cool! I believe in constant learning and it is an essential part of being a productive data enthusiast. I am also pursuing my masters in computer science from Simon Fraser University specializing in Big Data. Moreover, knowledge is transfer learning (pun intended!) and what I have gained, I plan on reflecting it back to the data community.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Rav Ahuja

Global Program Director, IBM Skills Network

Rav Ahuja is a Global Program Director at IBM. He leads growth strategy, curriculum creation, and partner programs for the IBM Skills Network. Rav co-founded Cognitive Class, an IBM led initiative to democratize skills for in demand technologies. He is based out of the IBM Canada Lab in Toronto and specializes in instructional solutions for AI, Data, Software Engineering and Cloud. Rav presents at events worldwide and has authored numerous papers, articles, books and courses on subjects in managing and analyzing data. Rav holds B. Eng. from McGill University and MBA from University of Western Ontario.

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