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⇱ Convolutional Neural Networks | Coursera


Convolutional Neural Networks

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Convolutional Neural Networks

This course is part of Deep Learning Specialization

569,473 already enrolled

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Gain insight into a topic and learn the fundamentals.
4.9

42,593 reviews

Intermediate level

Recommended experience

Flexible schedule
4 weeks at 10 hours a week
Learn at your own pace
95%
Most learners liked this course

Gain insight into a topic and learn the fundamentals.
4.9

42,593 reviews

Intermediate level

Recommended experience

Flexible schedule
4 weeks at 10 hours a week
Learn at your own pace
95%
Most learners liked this course

Build your subject-matter expertise

This course is part of the Deep Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

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.

Implement the foundational layers of CNNs (pooling, convolutions) and stack them properly in a deep network to solve multi-class image classification problems.

What's included

12 videos6 readings1 assignment2 programming assignments

12 videosβ€’Total 140 minutes
  • Computer Visionβ€’6 minutes
  • Edge Detection Exampleβ€’12 minutes
  • More Edge Detectionβ€’8 minutes
  • Paddingβ€’10 minutes
  • Strided Convolutionsβ€’9 minutes
  • Convolutions Over Volumeβ€’11 minutes
  • One Layer of a Convolutional Networkβ€’16 minutes
  • Simple Convolutional Network Exampleβ€’9 minutes
  • Pooling Layersβ€’10 minutes
  • CNN Exampleβ€’13 minutes
  • Why Convolutions?β€’10 minutes
  • Yann LeCun Interviewβ€’28 minutes
6 readingsβ€’Total 11 minutes
  • Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!β€’2 minutes
  • Clarifications about Upcoming Simple Convolutional Network Example Videoβ€’1 minute
  • Clarifications about Upcoming CNN Example Videoβ€’1 minute
  • Clarifications about Upcoming Why Convolutions?β€’1 minute
  • Lecture Notes W1β€’1 minute
  • (Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspaceβ€’5 minutes
1 assignmentβ€’Total 50 minutes
  • The Basics of ConvNets β€’50 minutes
2 programming assignmentsβ€’Total 360 minutes
  • Convolutional Model, Step by Stepβ€’180 minutes
  • Convolution Model Applicationβ€’180 minutes

Discover some powerful practical tricks and methods used in deep CNNs, straight from the research papers, then apply transfer learning to your own deep CNN.

What's included

14 videos3 readings1 assignment2 programming assignments

14 videosβ€’Total 127 minutes
  • Why look at case studies?β€’3 minutes
  • Classic Networksβ€’18 minutes
  • ResNetsβ€’7 minutes
  • Why ResNets Work?β€’9 minutes
  • Networks in Networks and 1x1 Convolutionsβ€’6 minutes
  • Inception Network Motivationβ€’10 minutes
  • Inception Networkβ€’9 minutes
  • MobileNetβ€’16 minutes
  • MobileNet Architectureβ€’9 minutes
  • EfficientNetβ€’4 minutes
  • Using Open-Source Implementationβ€’5 minutes
  • Transfer Learningβ€’9 minutes
  • Data Augmentationβ€’10 minutes
  • State of Computer Visionβ€’13 minutes
3 readingsβ€’Total 3 minutes
  • Clarifications about Upcoming Inception Network Motivation Videoβ€’1 minute
  • Lecture Notes W2β€’1 minute
  • Note on the Upcoming Programming Assignment - Residual Networksβ€’1 minute
1 assignmentβ€’Total 50 minutes
  • Deep Convolutional Models β€’50 minutes
2 programming assignmentsβ€’Total 360 minutes
  • Residual Networksβ€’180 minutes
  • Transfer Learning with MobileNetβ€’180 minutes

Apply your new knowledge of CNNs to one of the hottest (and most challenging!) fields in computer vision: object detection.

What's included

14 videos4 readings1 assignment2 programming assignments

14 videosβ€’Total 110 minutes
  • Object Localizationβ€’12 minutes
  • Landmark Detectionβ€’6 minutes
  • Object Detectionβ€’6 minutes
  • Convolutional Implementation of Sliding Windowsβ€’11 minutes
  • Bounding Box Predictionsβ€’14 minutes
  • Intersection Over Unionβ€’4 minutes
  • Non-max Suppressionβ€’8 minutes
  • Anchor Boxesβ€’10 minutes
  • YOLO Algorithmβ€’7 minutes
  • Region Proposals (Optional)β€’6 minutes
  • Semantic Segmentation with U-Netβ€’7 minutes
  • Transpose Convolutionsβ€’8 minutes
  • U-Net Architecture Intuitionβ€’3 minutes
  • U-Net Architectureβ€’8 minutes
4 readingsβ€’Total 13 minutes
  • Clarifications about Upcoming Convolutional Implementation of Sliding Windows Videoβ€’1 minute
  • Clarifications about Upcoming YOLO Algorithm Videoβ€’1 minute
  • Lecture Notes W3β€’1 minute
  • Clear Output Before Submitting (For U-Net Assignment)β€’10 minutes
1 assignmentβ€’Total 50 minutes
  • Detection Algorithms β€’50 minutes
2 programming assignmentsβ€’Total 360 minutes
  • Car detection with YOLOβ€’180 minutes
  • Image Segmentation with U-Netβ€’180 minutes

Explore how CNNs can be applied to multiple fields, including art generation and face recognition, then implement your own algorithm to generate art and recognize faces!

What's included

11 videos6 readings1 assignment2 programming assignments

11 videosβ€’Total 75 minutes
  • What is Face Recognition?β€’5 minutes
  • One Shot Learningβ€’5 minutes
  • Siamese Networkβ€’5 minutes
  • Triplet Lossβ€’15 minutes
  • Face Verification and Binary Classificationβ€’6 minutes
  • What is Neural Style Transfer?β€’2 minutes
  • What are deep ConvNets learning?β€’8 minutes
  • Cost Functionβ€’4 minutes
  • Content Cost Functionβ€’4 minutes
  • Style Cost Functionβ€’13 minutes
  • 1D and 3D Generalizationsβ€’9 minutes
6 readingsβ€’Total 25 minutes
  • Clarifications about Upcoming Face Verification and Binary Classification Videoβ€’1 minute
  • Clarifications about Upcoming Style Cost Function Videoβ€’1 minute
  • Lecture Notes W4β€’1 minute
  • [IMPORTANT] Reminder about end of access to Lab Notebooksβ€’2 minutes
  • Referencesβ€’10 minutes
  • Acknowledgmentsβ€’10 minutes
1 assignmentβ€’Total 50 minutes
  • Special Applications: Face Recognition & Neural Style Transfer β€’50 minutes
2 programming assignmentsβ€’Total 360 minutes
  • Face Recognitionβ€’180 minutes
  • Art Generation with Neural Style Transferβ€’180 minutes

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Instructors

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4.9 (3,236 ratings)

Top Instructor

DeepLearning.AI
51 Coursesβ€’9,803,410 learners

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Showing 3 of 42593

AV
Β·

Reviewed on Jul 11, 2020

I really enjoyed this course, it would be awesome to see al least one training example using GPU (maybe in Google Colab since not everyone owns one) so we could train the deepest networks from scratch

DD
Β·

Reviewed on Feb 6, 2020

very easy to understand and helped my understanding in Deep Learning-based computer vision. Yet, this course will need to be updated with new developments in the future (to catch up with the trend).

SH
Β·

Reviewed on Aug 5, 2019

Great content in lectures! Automatic graders for programming assignments can be tricky, and set to old versions of tf sometimes, but answers to these issues are readily found in the discussion forums.

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