このコースについて
4.9
24,315 ratings
2,741 reviews
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. After 3 weeks, you will: - Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance - Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization....
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次の専門講座における5コース2

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100%オンラインコース

自分のスケジュールですぐに学習を始めてください。
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柔軟性のある期限

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Beginner Level

初級レベル

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推奨:3 weeks, 3-6 hours per week

約13時間で修了
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字幕:English, Chinese (Traditional), Chinese (Simplified), Korean, Turkish

習得するスキル

HyperparameterTensorflowHyperparameter OptimizationDeep Learning
Stacks

次の専門講座における5コース2

Globe

100%オンラインコース

自分のスケジュールですぐに学習を始めてください。
Calendar

柔軟性のある期限

スケジュールに従って期限をリセットします。
Beginner Level

初級レベル

Clock

推奨:3 weeks, 3-6 hours per week

約13時間で修了
Comment Dots

English

字幕:English, Chinese (Traditional), Chinese (Simplified), Korean, Turkish

シラバス - 本コースの学習内容

1

セクション
Clock
8時間で修了

Practical aspects of Deep Learning

...
Reading
15本の動画(合計131分), 4 quizzes
Video15件のビデオ
Bias / Variance8 分
Basic Recipe for Machine Learning6 分
Regularization9 分
Why regularization reduces overfitting?7 分
Dropout Regularization9 分
Understanding Dropout7 分
Other regularization methods8 分
Normalizing inputs5 分
Vanishing / Exploding gradients6 分
Weight Initialization for Deep Networks6 分
Numerical approximation of gradients6 分
Gradient checking6 分
Gradient Checking Implementation Notes5 分
Yoshua Bengio interview25 分
Quiz1の練習問題
Practical aspects of deep learning20 分

2

セクション
Clock
4時間で修了

Optimization algorithms

...
Reading
11本の動画(合計92分), 2 quizzes
Video11件のビデオ
Understanding mini-batch gradient descent11 分
Exponentially weighted averages5 分
Understanding exponentially weighted averages9 分
Bias correction in exponentially weighted averages4 分
Gradient descent with momentum9 分
RMSprop7 分
Adam optimization algorithm7 分
Learning rate decay6 分
The problem of local optima5 分
Yuanqing Lin interview13 分
Quiz1の練習問題
Optimization algorithms20 分

3

セクション
Clock
5時間で修了

Hyperparameter tuning, Batch Normalization and Programming Frameworks

...
Reading
11本の動画(合計104分), 2 quizzes
Video11件のビデオ
Using an appropriate scale to pick hyperparameters8 分
Hyperparameters tuning in practice: Pandas vs. Caviar6 分
Normalizing activations in a network8 分
Fitting Batch Norm into a neural network12 分
Why does Batch Norm work?11 分
Batch Norm at test time5 分
Softmax Regression11 分
Training a softmax classifier10 分
Deep learning frameworks4 分
TensorFlow16 分
Quiz1の練習問題
Hyperparameter tuning, Batch Normalization, Programming Frameworks20 分
4.9
Direction Signs

36%

コース終了後に新しいキャリアをスタートした
Briefcase

83%

コースが具体的なキャリアアップにつながった

人気のレビュー

by CVDec 24th 2017

Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow\n\nThanks.

by PGOct 31st 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.

講師

Andrew Ng

Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/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.ai

deeplearning.aiについて

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

Deep Learningの専門講座について

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....
Deep Learning

よくある質問

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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.

さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。