Informações sobre o curso
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Nível intermediário

Aprox. 18 horas para completar

Inglês

Legendas: Chinês (tradicional), Árabe, Francês, Ucraniano, Chinês (simplificado), Portuguese (Brazilian), Coreano, Turco, Inglês, Japonês...

Habilidades que você terá

Artificial Neural NetworkBackpropagationPython ProgrammingDeep Learning

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 18 horas para completar

Inglês

Legendas: Chinês (tradicional), Árabe, Francês, Ucraniano, Chinês (simplificado), Portuguese (Brazilian), Coreano, Turco, Inglês, Japonês...

Programa - O que você aprenderá com este curso

Semana
1
2 horas para concluir

Introduction to deep learning

Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today.

...
7 vídeos ((Total 76 mín.)), 2 leituras, 1 teste
7 videos
What is a neural network?7min
Supervised Learning with Neural Networks8min
Why is Deep Learning taking off?10min
About this Course2min
Course Resources1min
Geoffrey Hinton interview40min
2 leituras
Frequently Asked Questions10min
How to use Discussion Forums10min
1 exercício prático
Introduction to deep learning20min
Semana
2
7 horas para concluir

Neural Networks Basics

Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.

...
19 vídeos ((Total 161 mín.)), 2 leituras, 3 testes
19 videos
Logistic Regression5min
Logistic Regression Cost Function8min
Gradient Descent11min
Derivatives7min
More Derivative Examples10min
Computation graph3min
Derivatives with a Computation Graph14min
Logistic Regression Gradient Descent6min
Gradient Descent on m Examples8min
Vectorization8min
More Vectorization Examples6min
Vectorizing Logistic Regression7min
Vectorizing Logistic Regression's Gradient Output9min
Broadcasting in Python11min
A note on python/numpy vectors6min
Quick tour of Jupyter/iPython Notebooks3min
Explanation of logistic regression cost function (optional)7min
Pieter Abbeel interview16min
2 leituras
Deep Learning Honor Code2min
Programming Assignment FAQ10min
1 exercício prático
Neural Network Basics20min
Semana
3
5 horas para concluir

Shallow neural networks

Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.

...
12 vídeos ((Total 109 mín.)), 2 testes
12 videos
Neural Network Representation5min
Computing a Neural Network's Output9min
Vectorizing across multiple examples9min
Explanation for Vectorized Implementation7min
Activation functions10min
Why do you need non-linear activation functions?5min
Derivatives of activation functions7min
Gradient descent for Neural Networks9min
Backpropagation intuition (optional)15min
Random Initialization7min
Ian Goodfellow interview14min
1 exercício prático
Shallow Neural Networks20min
Semana
4
5 horas para concluir

Deep Neural Networks

Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.

...
8 vídeos ((Total 64 mín.)), 3 testes
8 videos
Forward Propagation in a Deep Network7min
Getting your matrix dimensions right11min
Why deep representations?10min
Building blocks of deep neural networks8min
Forward and Backward Propagation10min
Parameters vs Hyperparameters7min
What does this have to do with the brain?3min
1 exercício prático
Key concepts on Deep Neural Networks20min
4.9
10574 avaliaçõesChevron Right

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comecei uma nova carreira após concluir estes cursos

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consegui um benefício significativo de carreira com este curso

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Principais avaliações do Neural Networks and Deep Learning

por GCMay 31st 2019

I have learnt a lot of tricks with numpy and I believe I have a better understanding of what a NN does. Now it does not look like a black box anymore. I look forward to see what's in the next courses!

por SSNov 27th 2017

Fantastic introduction to deep NNs starting from the shallow case of logistic regression and generalizing across multiple layers. The material is very well structured and Dr. Ng is an amazing teacher.

Instrutores

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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

Sobre 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 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....
Aprendizagem profunda

Perguntas Frequentes – FAQ

  • 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.

  • 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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