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.
Этот курс входит в специализацию ''Специализация Deep Learning'
Convolutional Neural Networks
от партнера
Об этом курсе
4.8
Оценки: 15,593
Приобретаемые навыки
Facial Recognition SystemTensorflowConvolutional Neural NetworkArtificial Neural Network
Программа курса: что вы изучите
Неделя
1Foundations of Convolutional Neural Networks
Learn to implement the foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep network to solve multi-class image classification problems.
12 видео (всего 140 мин.), 3 тестов
12 видео
Computer Vision5мин
Edge Detection Example11мин
More Edge Detection7мин
Padding9мин
Strided Convolutions9мин
Convolutions Over Volume10мин
One Layer of a Convolutional Network16мин
Simple Convolutional Network Example8мин
Pooling Layers10мин
CNN Example12мин
Why Convolutions?9мин
Yann LeCun Interview27мин
1 практическое упражнение
The basics of ConvNets20мин
Неделя
2Deep convolutional models: case studies
Learn about the practical tricks and methods used in deep CNNs straight from the research papers.
11 видео (всего 99 мин.), 2 тестов
11 видео
Classic Networks18мин
ResNets7мин
Why ResNets Work9мин
Networks in Networks and 1x1 Convolutions6мин
Inception Network Motivation10мин
Inception Network8мин
Using Open-Source Implementation4мин
Transfer Learning8мин
Data Augmentation9мин
State of Computer Vision12мин
1 практическое упражнение
Deep convolutional models20мин
Неделя
3Object detection
Learn how to apply your knowledge of CNNs to one of the toughest but hottest field of computer vision: Object detection.
10 видео (всего 85 мин.), 2 тестов
10 видео
Object Localization11мин
Landmark Detection5мин
Object Detection5мин
Convolutional Implementation of Sliding Windows11мин
Bounding Box Predictions14мин
Intersection Over Union4мин
Non-max Suppression8мин
Anchor Boxes9мин
YOLO Algorithm7мин
(Optional) Region Proposals6мин
1 практическое упражнение
Detection algorithms20мин
Неделя
4Special applications: Face recognition & Neural style transfer
Discover how CNNs can be applied to multiple fields, including art generation and face recognition. Implement your own algorithm to generate art and recognize faces!
11 видео (всего 76 мин.), 3 тестов
11 видео
One Shot Learning4мин
Siamese Network4мин
Triplet Loss15мин
Face Verification and Binary Classification6мин
What is neural style transfer?2мин
What are deep ConvNets learning?7мин
Cost Function3мин
Content Cost Function3мин
Style Cost Function13мин
1D and 3D Generalizations9мин
1 практическое упражнение
Special applications: Face recognition & Neural style transfer20мин
4.8
38%
83%
Лучшие рецензии
автор: EB•Nov 3rd 2017
Wonderful course. Covers a wide array of immediately appealing subjects: from object detection to face recognition to neural style transfer, intuitively motivate relevant models like YOLO and ResNet.
автор: DG•Feb 14th 2018
Too much hand-holding during assignments, although still very good directions. Obviously the issue with the final programming assignment needs to be addressed. Fantastic lecture material, as always.
Преподавателя
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.aiО deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
О специализации ''Deep Learning'
If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.
You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.
AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.
We will help you master Deep Learning, understand how to apply it, and build a career in AI.
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