Convolutional Neural Networks
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. By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.
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- Level Professional
- Duration 36 hours
- Course by DeepLearning.AI
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Offered by
About
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. By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.Modules
Convolutional Neural Networks
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11
Videos
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4
Readings
11
Videos
- 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?
4
Readings
- [IMPORTANT] Have questions, issues or ideas? Join our Forum!
- Clarifications about Upcoming Simple Convolutional Network Example Video
- Clarifications about Upcoming CNN Example Video
- Clarifications about Upcoming Why Convolutions?
Lecture Notes (Optional)
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1
Readings
1
Readings
- Lecture Notes W1
Quiz
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1
Assignment
1
Assignment
- The Basics of ConvNets
Programming Assignments
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1
Readings
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2
Programming
- Convolutional Model, Step by Step
- Convolution Model Application
1
Readings
- (Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspace
Heroes of Deep Learning (Optional)
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1
Videos
1
Videos
- Yann LeCun Interview
Case Studies
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10
Videos
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1
Readings
10
Videos
- 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
1
Readings
- Clarifications about Upcoming Inception Network Motivation Video
Practical Advice for Using ConvNets
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4
Videos
4
Videos
- Using Open-Source Implementation
- Transfer Learning
- Data Augmentation
- State of Computer Vision
Lecture Notes (Optional)
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1
Readings
1
Readings
- Lecture Notes W2
Quiz
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1
Assignment
1
Assignment
- Deep Convolutional Models
Programming Assignments
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1
Readings
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2
Programming
- Residual Networks
- Transfer Learning with MobileNet
1
Readings
- Note on the Upcoming Programming Assignment - Residual Networks
Detection Algorithms
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14
Videos
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2
Readings
14
Videos
- 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
2
Readings
- Clarifications about Upcoming Convolutional Implementation of Sliding Windows Video
- Clarifications about Upcoming YOLO Algorithm Video
Lecture Notes (Optional)
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1
Readings
1
Readings
- Lecture Notes W3
Quiz
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1
Assignment
1
Assignment
- Detection Algorithms
Programming Assignments
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1
Readings
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2
Programming
- Car detection with YOLO
- Image Segmentation with U-Net
1
Readings
- Clear Output Before Submitting (For U-Net Assignment)
Face Recognition
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5
Videos
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1
Readings
5
Videos
- What is Face Recognition?
- One Shot Learning
- Siamese Network
- Triplet Loss
- Face Verification and Binary Classification
1
Readings
- Clarifications about Upcoming Face Verification and Binary Classification Video
Neural Style Transfer
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6
Videos
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1
Readings
6
Videos
- What is Neural Style Transfer?
- What are deep ConvNets learning?
- Cost Function
- Content Cost Function
- Style Cost Function
- 1D and 3D Generalizations
1
Readings
- Clarifications about Upcoming Style Cost Function Video
Lecture Notes (Optional)
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1
Readings
1
Readings
- Lecture Notes W4
Quiz
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1
Assignment
1
Assignment
- Special Applications: Face Recognition & Neural Style Transfer
End of access to Lab Notebooks
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1
Readings
1
Readings
- [IMPORTANT] Reminder about end of access to Lab Notebooks
Programming Assignments
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2
Programming
- Face Recognition
- Art Generation with Neural Style Transfer
References & Acknowledgments
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2
Readings
2
Readings
- References
- Acknowledgments
Auto Summary
Enhance your expertise in computer vision with the "Convolutional Neural Networks" course, part of Coursera's Deep Learning Specialization. Led by top instructors, this professional-level program delves into building and applying convolutional neural networks for tasks like autonomous driving and face recognition. Through 2160 minutes of comprehensive content, you'll master visual detection, recognition, and neural style transfer. Available through Starter, Professional, and Paid subscriptions, this course is ideal for those looking to advance their AI and machine learning careers.
Andrew Ng
Kian Katanforoosh
Younes Bensouda Mourri