Deep Learning Fundamentals with Keras

This course is a part of Deep Learning, a 7-course Professional Certificate series from edX.

Please Note: Learners who successfully complete this IBM course can earn a skill badge —a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

Looking to kickstart a career in deep learning? Look no further. This course will introduce you to the field of deep learning and teach you the fundamentals. You will learn about some of the exciting applications of deep learning, the basics fo neural networks, different deep learning models, and how to build your first deep learning model using the easy yet powerful library Keras.

This course will presentsimplified explanations to some oftoday's hottest topics in data science, including:

What is deep learning?

How do neural networks learn and what are activation functions?

What are deep learning libraries and how do they compare to one another?

What are supervised and unsupervised deep learning models?

How to use Keras to build, train, and test deep learning models?

The demand fordeep learning skills-- and the job salaries of deep learning practitioners -- arecontinuing to grow, as AI becomes more pervasive in our societies. This course will help you build the knowledge you need to future-proofyour career.

What you'll learn

• You will learn about exciting applications of deep learning and why it is really rewarding to learn how to leverage deep learning skills.
• You will learn about neural networks and how theylearn and update their weights and biases.
• You will learn about thevanishing gradient problem.
• You will learn about building a regression model using the Keras library.
• You will learn about building a classification model using the Keras library.
• You will learn about supervised deep learning models, such as convolutional neural networks and recurrent neural networks, and how to build a convolutional neural network using the Keras library.
• You will learn about unsupervised learning models such as autoencoders.