Course Summary
This course teaches the fundamentals of Convolutional Neural Networks (CNN) and their applications in computer vision tasks such as image and video recognition.Key Learning Points
- Learn about convolutional neural networks and their applications in computer vision.
- Understand the architecture of convolutional neural networks.
- Implement and train convolutional neural networks using TensorFlow.
Related Topics for further study
Learning Outcomes
- Understand the basics of convolutional neural networks and their applications in computer vision
- Learn how to implement and train convolutional neural networks using TensorFlow
- Be able to apply convolutional neural networks to real-world computer vision problems
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Basic understanding of machine learning concepts
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
- Video lectures
- Assignments and quizzes
Similar Courses
- Deep Learning
- Applied Data Science with Python
Related Education Paths
Notable People in This Field
- Yann LeCun
- Fei-Fei Li
Related Books
Description
In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.
Outline
- Foundations of Convolutional Neural Networks
- Computer Vision
- Edge Detection Example
- More Edge Detection
- Padding
- Strided Convolutions
- Convolutions Over Volume
- One Layer of a Convolutional Network
- Simple Convolutional Network Example
- Pooling Layers
- CNN Example
- Why Convolutions?
- Yann LeCun Interview
- Connect with your Mentors and Fellow Learners on Discourse!
- Strided convolutions *CORRECTION*
- Simple Convolutional Network Example *CORRECTION*
- CNN Example *CORRECTION*
- Why Convolutions? *CORRECTION*
- Lectures in PDF
- How to Download your Notebook
- H​ow to Refresh your Workspace
- The Basics of ConvNets
- Deep Convolutional Models: Case Studies
- Why look at case studies?
- Classic Networks
- ResNets
- Why ResNets Work?
- Networks in Networks and 1x1 Convolutions
- Inception Network Motivation
- Inception Network
- MobileNet
- MobileNet Architecture
- EfficientNet
- Using Open-Source Implementation
- Transfer Learning
- Data Augmentation
- State of Computer Vision
- Inception Network Motivation *CORRECTION*
- Lectures in PDF
- Deep Convolutional Models
- Object Detection
- Object Localization
- Landmark Detection
- Object Detection
- Convolutional Implementation of Sliding Windows
- Bounding Box Predictions
- Intersection Over Union
- Non-max Suppression
- Anchor Boxes
- YOLO Algorithm
- Region Proposals (Optional)
- Semantic Segmentation with U-Net
- Transpose Convolutions
- U-Net Architecture Intuition
- U-Net Architecture
- Convolutional Implementation of Sliding Windows *CORRECTION*
- YOLO algorithm *CORRECTION*
- Lectures in PDF
- Clear Output Before Submitting (For U-Net Assignment)
- Detection Algorithms
- Special Applications: Face recognition & Neural Style Transfer
- What is Face Recognition?
- One Shot Learning
- Siamese Network
- Triplet Loss
- Face Verification and Binary Classification
- What is Neural Style Transfer?
- What are deep ConvNets learning?
- Cost Function
- Content Cost Function
- Style Cost Function
- 1D and 3D Generalizations
- Triplet Loss *CORRECTION*
- Face Verification and Binary Classification *CORRECTION*
- Style Cost *CORRECTION*
- Lectures in PDF
- References
- Acknowledgments
- Special Applications: Face Recognition & Neural Style Transfer
Summary of User Reviews
Discover the power of Convolutional Neural Networks with Coursera's course. Students praise the course for its comprehensive approach to the subject matter and its relevance to real-world applications.Key Aspect Users Liked About This Course
Comprehensive approach to the subject matterPros from User Reviews
- Excellent course for beginners and professionals alike
- Hands-on assignments help to reinforce concepts
- Instructors are knowledgeable and engaging
- Real-world examples make the subject matter relevant
- Course structure is well-organized and easy to follow
Cons from User Reviews
- Some students found the course to be too challenging
- Technical issues with the platform were reported by a few users
- Some students would have preferred more in-depth coverage of certain topics
- Course content may be too basic for advanced students
- Course materials are only available in English
Andrew NgTop Instructor
4.9 Raiting
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