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.
Ce cours fait partie de la Spécialisation Deep Learning
Convolutional Neural Networks
proposé par
À propos de ce cours
4.8
16,174 notes
Compétences que vous acquerrez
Facial Recognition SystemTensorflowConvolutional Neural NetworkArtificial Neural Network
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
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 videos (Total 140 min), 3 quiz
12 vidéos
Computer Vision5 min
Edge Detection Example11 min
More Edge Detection7 min
Padding9 min
Strided Convolutions9 min
Convolutions Over Volume10 min
One Layer of a Convolutional Network16 min
Simple Convolutional Network Example8 min
Pooling Layers10 min
CNN Example12 min
Why Convolutions?9 min
Yann LeCun Interview27 min
1 exercice pour s'entraîner
The basics of ConvNets20 min
Semaine
2Deep convolutional models: case studies
Learn about the practical tricks and methods used in deep CNNs straight from the research papers.
11 videos (Total 99 min), 2 quiz
11 vidéos
Classic Networks18 min
ResNets7 min
Why ResNets Work9 min
Networks in Networks and 1x1 Convolutions6 min
Inception Network Motivation10 min
Inception Network8 min
Using Open-Source Implementation4 min
Transfer Learning8 min
Data Augmentation9 min
State of Computer Vision12 min
1 exercice pour s'entraîner
Deep convolutional models20 min
Semaine
3Object detection
Learn how to apply your knowledge of CNNs to one of the toughest but hottest field of computer vision: Object detection.
10 videos (Total 85 min), 2 quiz
10 vidéos
Object Localization11 min
Landmark Detection5 min
Object Detection5 min
Convolutional Implementation of Sliding Windows11 min
Bounding Box Predictions14 min
Intersection Over Union4 min
Non-max Suppression8 min
Anchor Boxes9 min
YOLO Algorithm7 min
(Optional) Region Proposals6 min
1 exercice pour s'entraîner
Detection algorithms20 min
Semaine
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 videos (Total 76 min), 3 quiz
11 vidéos
One Shot Learning4 min
Siamese Network4 min
Triplet Loss15 min
Face Verification and Binary Classification6 min
What is neural style transfer?2 min
What are deep ConvNets learning?7 min
Cost Function3 min
Content Cost Function3 min
Style Cost Function13 min
1D and 3D Generalizations9 min
1 exercice pour s'entraîner
Special applications: Face recognition & Neural style transfer20 min
4.8
2,128 avis38%
a commencé une nouvelle carrière après avoir terminé ces cours
83%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
Meilleurs avis
par 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.
par 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.
Enseignants
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.aiÀ propos de deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
À propos de la Spécialisation 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.
Foire Aux Questions
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Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
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