Neural Networks and Deep Learning

Course Image
Out of 40,137 reviews
Go to course
or more from Coursera

Course Information

Platform iconPlatform: Coursera
Level iconDifficulty: Intermediate
Time icon26-40h hours of content
Speed iconStarts Nov 23
Certificate iconCertificate: Certificate (q2)
Instructor Image
Andrew Ng

Related Courses

Machine Learning
by Coursera | $43/mo.

This is a fantastic course that functions as an introduction to machine learning. Machine learning is at the core of the new trends we see these days in self-driving cars, image recognition, web search, and more. Besides the theoretical background of machine learning, the student will learn how to implement these techniques in practice.

Neural Networks and Deep Learning
by Coursera | $43/mo.

"Neural Networks and Deep Learning" by Coursera is an excellent course that helps you understand the major technology trends in Deep Learning and teaches your how to build, train, and implement neural networks. This is more than a surface-level course, diving deep into the fundamentals of what makes Deep Learning work.

Deep Learning Specialization
by Coursera | $43/mo.

If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projec...

Learning How to Learn: Powerful mental tools to help you master tough subjects
by Coursera | $43/mo.

A course that teaches you about learning. This might sound a bit weird but being able to learn and acquire new skills is one of the most valuable assets any professional can have. This elaborate Coursera course covers everything there is to know about taking in information, diving into memory techniques, procrastination, and best practices to master subjects. An absolute recommender.

Digital Marketing Career Track
by Springboard | $3300

One of a few career tracks that is provided by Springboard, this one in particular gets you job-ready for a profession in Digital Marketing. Learn how to drive revenu and acquire customers by learning Digital Marketing skills from the best experts in the field.

Neural Networks and Deep Learning

This course is created or produced by via Coursera

Description of the course

If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. This is the first course of the Deep Learning Specialization.

Syllabus of "Neural Networks and Deep Learning"

Introduction to deep learning

Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today.

Neural Networks Basics

Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.

Shallow neural networks

Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.

Deep Neural Networks

Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.

Go to course
Back to search results