This course is part of the IBM AI Engineering Professional Certificate

Alex Aklson

Offered By

IBM AI Engineering Professional CertificateIBM

About this Course

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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.
After completing this course, learners will be able to:
•	describe what a neural network is, what a deep learning model is, and the difference between them.
•	demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines.
•	demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks.
•	build deep learning models and networks using the Keras library.

Shareable Certificate

Earn a Certificate upon completion

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Course 3 of 6 in the

IBM AI Engineering

Flexible deadlines

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Intermediate Level

Approx. 8 hours to complete

English

Subtitles: French, Portuguese (Brazilian), Russian, English, Spanish

Skills you will gain

Deep LearningArtificial Neural NetworkArtificial Intelligence (AI)Machine Learningkeras

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 6 in the

IBM AI Engineering

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 8 hours to complete

English

Subtitles: French, Portuguese (Brazilian), Russian, English, Spanish

Instructor

Instructor rating

4.76/5 (76 Ratings)

Alex Aklson

Ph.D., Data Scientist

420,820 Learners

11 Courses

Offered by

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.

Syllabus - What you will learn from this course

Week

Week 1

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

Week

Week 2

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)

4 videos

Gradient Descent5m

Backpropagation9m

Vanishing Gradient1m

Activation Functions5m

1 practice exercise

Artificial Neural Networks30m

Week

Week 3

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)

3 videos

Deep Learning Libraries3m

Regression Models with Keras6m

Classification Models with Keras5m

1 practice exercise

Keras and Deep Learning Libraries30m

Week

Week 4

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

Shallow Versus Deep Neural Networks3m

Convolutional Neural Networks8m

Recurrent Neural Networks2m

Autoencoders2m

1 practice exercise

Deep Learning Models30m

About the IBM AI Engineering Professional Certificate

Artificial intelligence (AI) is revolutionizing entire industries, changing the way companies across sectors leverage data to make decisions. To stay competitive, organizations need qualified AI engineers who use cutting-edge methods like machine learning algorithms and deep learning neural networks to provide data driven actionable intelligence for their businesses. This 6-course Professional Certificate is designed to equip you with the tools you need to succeed in your career as an AI or ML engineer.

You’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers. Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data using Apache Spark. You’ll build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, and autoencoders. In addition to earning a Professional Certificate from Coursera, you will also receive a digital badge from IBM recognizing your proficiency in AI engineering.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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.

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Introduction to Deep Learning & Neural Networks with Keras