Neural Networks and Deep Learning

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Course Information

Platform iconPlatform: Coursera
Level iconDifficulty: Intermediate
Time icon26-40h hours of content
Speed iconStarts Jul 30
Certificate iconCertificate: Certificate (q2)
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Andrew Ng

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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.

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