This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow.
Este curso faz parte do Programa de cursos integrados Aprendizagem profunda
oferecido por
Programa de cursos integrados Aprendizagem profundadeeplearning.aiInformações sobre o curso
1,579,433 visualizações recentes
Resultados de carreira do aprendiz
41%
comecei uma nova carreira após concluir estes cursos
37%
consegui um benefício significativo de carreira com este curso
12%
recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 2 de 5 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível iniciante
Aprox. 18 horas para completar
Inglês
Legendas: Chinês (tradicional), Chinês (simplificado), Português (Brasil), Coreano, Turco, Inglês, Espanhol
Habilidades que você terá
HyperparameterTensorflowHyperparameter OptimizationDeep Learning
Resultados de carreira do aprendiz
41%
comecei uma nova carreira após concluir estes cursos
37%
consegui um benefício significativo de carreira com este curso
12%
recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 2 de 5 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível iniciante
Aprox. 18 horas para completar
Inglês
Legendas: Chinês (tradicional), Chinês (simplificado), Português (Brasil), Coreano, Turco, Inglês, Espanhol
oferecido por
deeplearning.ai
Founded by Andrew Ng, deeplearning.ai is on a mission to make world-class AI education accessible to people around the globe so that we can all benefit from an AI-powered future.
Programa - O que você aprenderá com este curso
8 horas para concluir
Practical aspects of Deep Learning
8 horas para concluir
15 vídeos (Total 131 mín.), 3 leituras, 4 testes
15 videos
Bias / Variance8min
Basic Recipe for Machine Learning6min
Regularization9min
Why regularization reduces overfitting?7min
Dropout Regularization9min
Understanding Dropout7min
Other regularization methods8min
Normalizing inputs5min
Vanishing / Exploding gradients6min
Weight Initialization for Deep Networks6min
Numerical approximation of gradients6min
Gradient checking6min
Gradient Checking Implementation Notes5min
Yoshua Bengio interview25min
3 leituras
Clarification about Upcoming Regularization Video1min
Clarification about Upcoming Understanding dropout Video1min
Clarification about Upcoming Normalizing Inputs Video1min
1 exercício prático
Practical aspects of deep learning30min
5 horas para concluir
Optimization algorithms
5 horas para concluir
11 vídeos (Total 92 mín.), 2 leituras, 2 testes
11 videos
Understanding mini-batch gradient descent11min
Exponentially weighted averages5min
Understanding exponentially weighted averages9min
Bias correction in exponentially weighted averages4min
Gradient descent with momentum9min
RMSprop7min
Adam optimization algorithm7min
Learning rate decay6min
The problem of local optima5min
Yuanqing Lin interview13min
2 leituras
Clarification about Upcoming Adam Optimization Video1min
Clarification about Learning Rate Decay Video1min
1 exercício prático
Optimization algorithms30min
5 horas para concluir
Hyperparameter tuning, Batch Normalization and Programming Frameworks
5 horas para concluir
11 vídeos (Total 104 mín.), 2 leituras, 2 testes
11 videos
Tuning process7min
Using an appropriate scale to pick hyperparameters8min
Hyperparameters tuning in practice: Pandas vs. Caviar6min
Normalizing activations in a network8min
Fitting Batch Norm into a neural network12min
Why does Batch Norm work?11min
Batch Norm at test time5min
Softmax Regression11min
Training a softmax classifier10min
Deep learning frameworks4min
TensorFlow16min
2 leituras
Clarifications about Upcoming Softmax Video1min
Note about TensorFlow 1 and TensorFlow 210min
1 exercício prático
Hyperparameter tuning, Batch Normalization, Programming Frameworks30min
Avaliações
Principais avaliações do IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION
por YL20 de Fev de 2018
very useful course, especially the last tensorflow assignment. the only reason i gave 4 stars is due to the lack of practice on batchnorm, which i believe is one of the most usefule techniques lately.
por XG30 de Out de 2017
Thank you Andrew!! I know start to use Tensorflow, however, this tool is not well for a research goal. Maybe, pytorch could be considered in the future!! And let us know how to use pytorch in Windows.
por AM8 de Out de 2019
I really enjoyed this course. Many details are given here that are crucial to gain experience and tips on things that looks easy at first sight but are important for a faster ML project implementation
por A22 de Out de 2017
Assignment in week 2 could not tell the difference between 'a-=b' and 'a=a-b' and marked the former as incorrect even though they are the same and gave the same output. Other than that, a great course
Sobre Programa de cursos integrados Aprendizagem profunda
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
When will I have access to the lectures and assignments?
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
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Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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