What you'll learn
Introduction to deep learning
Neural Networks Basics
Shallow neural networks
Deep Neural Networks
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.
Access to a computer or mobile device with an internet connection.
Motivation to learn!
There are no special materials or prerequisite knowledge required for this course.
Who this course is for
Students who are already familiar with this field
Students willing to put in a couple hours to learn about Neural Networks and Deep Learning
Advanced students wanting to add another skill to their portfolio
Andrew Ng – CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
Head Teaching Assistant – Kian Katanforoosh – Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
Teaching Assistant – Younes Bensouda Mourri – Mathematical & Computational Sciences, Stanford University, deeplearning.ai
This course includes
Option for learning at your own pace
Videos and reading material about the course
Assessed tasks with feedback from other course participants
Evaluated tests with feedback
Evaluated programming tasks