What you'll learn
Foundations of Convolutional Neural Networks
Deep convolutional models: case studies
Object detection
Special applications: Face recognition & Neural style transfer
Description
This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.
You will:
– Understand how to build a convolutional neural network, including recent variations such as residual networks.
– Know how to apply convolutional networks to visual detection and recognition tasks.
– Know to use neural style transfer to generate art.
– Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.
This is the fourth course of the Deep Learning Specialization.
Requirements
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 Convolutional Neural Networks
Advanced students wanting to add another skill to their portfolio
Content Creator
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
Participation Confirmation/Certificate
Option for learning at your own pace
Videos and reading material about the course
Practice tests
Assessed tasks with feedback from other course participants
Evaluated tests with feedback
Evaluated programming tasks
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