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.
This course is part of the IBM AI Engineering Professional Certificate
Introduction to Deep Learning & Neural Networks with Keras
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About this Course
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Skills you will gain
Deep LearningArtificial Neural NetworkArtificial Intelligence (AI)Machine Learningkeras
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Course 3 of 6 in the
Flexible deadlines
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Intermediate Level
Approx. 9 hours to complete
Suggested: 2 - 3 hours/week
English
Subtitles: English
Syllabus - What you will learn from this course
2 hours to complete
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.
2 hours to complete
4 videos (Total 17 min), 1 reading, 2 quizzes
4 videos
Welcome2m
Introduction to Deep Learning4m
Neurons and Neural Networks3m
Artificial Neural Networks5m
1 reading
Syllabus10m
1 practice exercise
Introduction to Neural Networks and Deep Learning30m
1 hour to complete
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.
1 hour to complete
4 videos (Total 22 min)
1 practice exercise
Artificial Neural Networks30m
3 hours to complete
Keras and Deep Learning Libraries
In this module, you will learn about the diifferent deep learning libraries namely, Keras, PyTorch, and TensorFlow. You will also learn how to build regression and classification models using the Keras library.
3 hours to complete
3 videos (Total 16 min)
1 practice exercise
Keras and Deep Learning Libraries30m
2 hours to complete
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.
2 hours to complete
4 videos (Total 17 min)
4 videos
Convolutional Neural Networks8m
Recurrent Neural Networks2m
Autoencoders2m
1 practice exercise
Deep Learning Models30m
Reviews
4.7
TOP REVIEWS FROM INTRODUCTION TO DEEP LEARNING & NEURAL NETWORKS WITH KERAS
by ABMar 15, 2020
Interesting course. Forward propagation, gradient descent, backward propagation, the vanishing gradient problem, (+ Regression, Classification, and CNN with Keras) explained clearly.
by MCFeb 19, 2020
Best suited for beginner in Deep learning with Keras. Good content, hands on experience with Jupiter notebook with IBM Developer tool. Worked Exercise code for CNN & RNN.
by FBMar 24, 2020
It is a really good course.\n\nThe labs could have had more for us to do, much of the labs was already implemented.\n\nStill, great introduction to the proposed subjects.
by ASMar 19, 2020
A good course. Could be better if it was explained how to select the optimal number of layers and nodes. This was not covered and explained anywhere. Overall it was good.
by SUMar 10, 2020
try to add more case study problems and solve it on lectures so that we can understand how to start (initialize) the coding part when we receive any real world problem.
by RSFeb 20, 2020
I took this course for understanding the TensorFlow properly. Now I am in the situation to understand all the frameworks. Thanks a lot for providing me this free course
by BJOct 9, 2019
Good practical examples for ANN. It could be improved the theoretical part and compare better the architecture of the networks with the algorithms and code for Keras
by ASDec 15, 2019
Queries are not getting resolved in the discussion forum. So, instructors should participate in the discussion forum to resolve such queries.
by VGFeb 1, 2020
This course is a piece of art. I see how carefully and precise course was build and recorded. Thank you for awesome experience!
by KHFeb 25, 2020
This course was great because first it covered theory then goes to code. And the algorithms are taught by the teacher so well.
by LDSep 7, 2019
The teaching is not deep enough to solve the Week 5 assignment, please take note you need to plumb in other Keras courses.
by CHDec 9, 2019
Really enjoyed the class, felt that the level of challenge was appropriate. Thanks for making this class available!
by VPFeb 25, 2020
The course contents and labs are very informative and explains the basic concepts throughly.
by GKOct 2, 2019
Very intractive and benificial course for me .Thank you coursera and IBM for this course
by RCNov 9, 2019
I really enjoyed this course, specially for all the labs and assignments. Thanks much!
by DUJan 15, 2020
Very good course to start with Neural Networks & Keral lib. Recommend for beginners.
by ATSep 27, 2019
Details very well covered. I found it very much interesting and well explained.
by PNov 5, 2019
It,s a very good course to get grip on keras and artificial neural networks.
by JPOct 4, 2019
Excellent course that is very well done. Final project was super hard.
by MNSep 25, 2019
Super Class course ever I see. Thanks for give such best production.
About IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
About the IBM AI Engineering Professional Certificate
The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning.
You will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
You will be able to scale Machine Learning on Big Data using Apache Spark. You will build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Certificate?
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
More questions? Visit the Learner Help Center.