Acerca de este Curso
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Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 21 horas para completar

Sugerido: 4 weeks of study, 4-5 hours/week...

Inglés (English)

Subtítulos: Chino (tradicional), Chino (simplificado), Coreano, Turco (Turkish), Inglés (English), Español (Spanish), Japonés...
User
Los estudiantes que toman este Course son
  • Data Scientists
  • Machine Learning Engineers
  • Biostatisticians
  • Researchers
  • Research Assistants

Habilidades que obtendrás

Facial Recognition SystemTensorflowConvolutional Neural NetworkArtificial Neural Network
User
Los estudiantes que toman este Course son
  • Data Scientists
  • Machine Learning Engineers
  • Biostatisticians
  • Researchers
  • Research Assistants

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 21 horas para completar

Sugerido: 4 weeks of study, 4-5 hours/week...

Inglés (English)

Subtítulos: Chino (tradicional), Chino (simplificado), Coreano, Turco (Turkish), Inglés (English), Español (Spanish), Japonés...

Programa - Qué aprenderás en este curso

Semana
1
6 horas para completar

Foundations of Convolutional Neural Networks

12 videos (Total 140 minutos), 4 lecturas, 3 cuestionarios
12 videos
Edge Detection Example11m
More Edge Detection7m
Padding9m
Strided Convolutions9m
Convolutions Over Volume10m
One Layer of a Convolutional Network16m
Simple Convolutional Network Example8m
Pooling Layers10m
CNN Example12m
Why Convolutions?9m
Yann LeCun Interview27m
4 lecturas
Strided convolutions *CORRECTION*1m
Simple Convolutional Network Example *CORRECTION*1m
CNN Example *CORRECTION*1m
Why Convolutions? *CORRECTION*1m
1 ejercicio de práctica
The basics of ConvNets20m
Semana
2
5 horas para completar

Deep convolutional models: case studies

11 videos (Total 99 minutos), 1 lectura, 2 cuestionarios
11 videos
Classic Networks18m
ResNets7m
Why ResNets Work9m
Networks in Networks and 1x1 Convolutions6m
Inception Network Motivation10m
Inception Network8m
Using Open-Source Implementation4m
Transfer Learning8m
Data Augmentation9m
State of Computer Vision12m
1 lectura
Inception Network Motivation *CORRECTION*1m
1 ejercicio de práctica
Deep convolutional models20m
Semana
3
4 horas para completar

Object detection

10 videos (Total 85 minutos), 2 lecturas, 2 cuestionarios
10 videos
Landmark Detection5m
Object Detection5m
Convolutional Implementation of Sliding Windows11m
Bounding Box Predictions14m
Intersection Over Union4m
Non-max Suppression8m
Anchor Boxes9m
YOLO Algorithm7m
(Optional) Region Proposals6m
2 lecturas
Convolutional Implementation of Sliding Windows *CORRECTION*1m
YOLO algorithm *CORRECTION*1m
1 ejercicio de práctica
Detection algorithms20m
Semana
4
5 horas para completar

Special applications: Face recognition & Neural style transfer

11 videos (Total 76 minutos), 3 lecturas, 3 cuestionarios
11 videos
One Shot Learning4m
Siamese Network4m
Triplet Loss15m
Face Verification and Binary Classification6m
What is neural style transfer?2m
What are deep ConvNets learning?7m
Cost Function3m
Content Cost Function3m
Style Cost Function13m
1D and 3D Generalizations9m
3 lecturas
Triplet Loss *CORRECTION*1m
Face Verification and Binary Classification *CORRECTION*1m
Style Cost *CORRECTION*1m
1 ejercicio de práctica
Special applications: Face recognition & Neural style transfer20m
4.9
3165 revisionesChevron Right

37%

comenzó una nueva carrera después de completar estos cursos

37%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

12%

consiguió un aumento de sueldo o ascenso

Principales revisiones sobre Convolutional Neural Networks

por RKSep 2nd 2019

This is very intensive and wonderful course on CNN. No other course in the MOOC world can be compared to this course's capability of simplifying complex concepts and visualizing them to get intuition.

por AGJan 13th 2019

Great course for kickoff into the world of CNN's. Gives a nice overview of existing architectures and certain applications of CNN's as well as giving some solid background in how they work internally.

Instructores

Avatar

Andrew Ng

CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
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Head Teaching Assistant - Kian Katanforoosh

Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
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Teaching Assistant - Younes Bensouda Mourri

Mathematical & Computational Sciences, Stanford University, deeplearning.ai
Computer Science

Acerca de deeplearning.ai

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

Acerca de Programa especializado Aprendizaje profundo

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....
Aprendizaje profundo

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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