DeepLearning.AI - Convolutional Neural Networks
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Convolutional Neural Networks at Coursera Overview
Duration | 20 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Convolutional Neural Networks at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 4 of 5 in the Deep Learning Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures A basic grasp of linear algebra & ML
- Approx. 20 hours to complete
- English Subtitles: Chinese (Traditional), Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish, Japanese
Convolutional Neural Networks at Coursera Course details
- 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.
Convolutional Neural Networks at Coursera Curriculum
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
Strided convolutions *CORRECTION*
Simple Convolutional Network Example *CORRECTION*
CNN Example *CORRECTION*
Why Convolutions? *CORRECTION*
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
Using Open-Source Implementation
Transfer Learning
Data Augmentation
State of Computer Vision
Inception Network Motivation *CORRECTION*
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
(Optional) Region Proposals
Convolutional Implementation of Sliding Windows *CORRECTION*
YOLO algorithm *CORRECTION*
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*
Special applications: Face recognition & Neural style transfer
Convolutional Neural Networks at Coursera Admission Process
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