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.
Informações sobre o curso
4.8
16,300 classificações
Habilidades que você terá
Facial Recognition SystemTensorflowConvolutional Neural NetworkArtificial Neural Network
Programa - O que você aprenderá com este curso
Semana
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 vídeos (total de (Total 140 mín.) min), 3 testes
12 videos
Computer Vision5min
Edge Detection Example11min
More Edge Detection7min
Padding9min
Strided Convolutions9min
Convolutions Over Volume10min
One Layer of a Convolutional Network16min
Simple Convolutional Network Example8min
Pooling Layers10min
CNN Example12min
Why Convolutions?9min
Yann LeCun Interview27min
1 exercício prático
The basics of ConvNets20min
Semana
2Deep convolutional models: case studies
Learn about the practical tricks and methods used in deep CNNs straight from the research papers.
11 vídeos (total de (Total 99 mín.) min), 2 testes
11 videos
Classic Networks18min
ResNets7min
Why ResNets Work9min
Networks in Networks and 1x1 Convolutions6min
Inception Network Motivation10min
Inception Network8min
Using Open-Source Implementation4min
Transfer Learning8min
Data Augmentation9min
State of Computer Vision12min
1 exercício prático
Deep convolutional models20min
Semana
3Object detection
Learn how to apply your knowledge of CNNs to one of the toughest but hottest field of computer vision: Object detection.
10 vídeos (total de (Total 85 mín.) min), 2 testes
10 videos
Object Localization11min
Landmark Detection5min
Object Detection5min
Convolutional Implementation of Sliding Windows11min
Bounding Box Predictions14min
Intersection Over Union4min
Non-max Suppression8min
Anchor Boxes9min
YOLO Algorithm7min
(Optional) Region Proposals6min
1 exercício prático
Detection algorithms20min
Semana
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 vídeos (total de (Total 76 mín.) min), 3 testes
11 videos
One Shot Learning4min
Siamese Network4min
Triplet Loss15min
Face Verification and Binary Classification6min
What is neural style transfer?2min
What are deep ConvNets learning?7min
Cost Function3min
Content Cost Function3min
Style Cost Function13min
1D and 3D Generalizations9min
1 exercício prático
Special applications: Face recognition & Neural style transfer20min
38%
comecei uma nova carreira após concluir estes cursos
36%
consegui um benefício significativo de carreira com este curso
Melhores avaliações
por 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.
por 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.
Instrutores
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.aiSobre deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
Sobre o Programa de cursos integrados 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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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Existe algum auxílio financeiro disponível?
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