Introduction to Deep Learning & Neural Networks with Keras
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.
None
Syllabus
Syllabus - What you will learn from this course
Week 1
Introduction to Neural Networks and Deep Learning
Week 2
Artificial Neural Networks
Week 3
Keras and Deep Learning Libraries
Week 4
Deep Learning Models
Week 5
Course Project
FAQ
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:
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.
Reviews
In-depth concept-analysis is required. Good for people who know the theory and want to learn and revise its implementation in Python.
It is a good insight for someone to know and understand Deep learning. And exams and projects make sure students learn and practice new concepts.
Nice overview. The coding exercises could be deeper, and in the second half of the course lose any depth at all. Understandable for such a short course, but still felt like a missed opportunity.
Excellent course. The instructor was clearly passaionate about the topics covered and very knowledeable. A well designed course that was easy to understand and follow.
