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.
提供方
深度学习 專項課程deeplearning.ai課程信息
1,609,096 次近期查看
學生職業成果
41%
完成這些課程後已開始新的職業生涯
37%
通過此課程獲得實實在在的工作福利
12%
加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 2 門課程（共 5 門）
可靈活調整截止日期
根據您的日程表重置截止日期。
初級
完成時間大約為18 小時
英語（English）
字幕：中文（繁體）, 中文（簡體）, 巴西葡萄牙語, 韓語, 土耳其語（Turkish）, 英語（English）, 西班牙語（Spanish）
您將獲得的技能
HyperparameterTensorflowHyperparameter OptimizationDeep Learning
學生職業成果
41%
完成這些課程後已開始新的職業生涯
37%
通過此課程獲得實實在在的工作福利
12%
加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 2 門課程（共 5 門）
可靈活調整截止日期
根據您的日程表重置截止日期。
初級
完成時間大約為18 小時
英語（English）
字幕：中文（繁體）, 中文（簡體）, 巴西葡萄牙語, 韓語, 土耳其語（Turkish）, 英語（English）, 西班牙語（Spanish）
提供方
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.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 8 小時
Practical aspects of Deep Learning
完成時間為 8 小時
15 個視頻 （總計 131 分鐘）, 3 個閱讀材料, 4 個測驗
15 個視頻
Bias / Variance8分鐘
Basic Recipe for Machine Learning6分鐘
Regularization9分鐘
Why regularization reduces overfitting?7分鐘
Dropout Regularization9分鐘
Understanding Dropout7分鐘
Other regularization methods8分鐘
Normalizing inputs5分鐘
Vanishing / Exploding gradients6分鐘
Weight Initialization for Deep Networks6分鐘
Numerical approximation of gradients6分鐘
Gradient checking6分鐘
Gradient Checking Implementation Notes5分鐘
Yoshua Bengio interview25分鐘
3 個閱讀材料
Clarification about Upcoming Regularization Video1分鐘
Clarification about Upcoming Understanding dropout Video1分鐘
Clarification about Upcoming Normalizing Inputs Video1分鐘
1 個練習
Practical aspects of deep learning30分鐘
完成時間為 5 小時
Optimization algorithms
完成時間為 5 小時
11 個視頻 （總計 92 分鐘）, 2 個閱讀材料, 2 個測驗
11 個視頻
Understanding mini-batch gradient descent11分鐘
Exponentially weighted averages5分鐘
Understanding exponentially weighted averages9分鐘
Bias correction in exponentially weighted averages4分鐘
Gradient descent with momentum9分鐘
RMSprop7分鐘
Adam optimization algorithm7分鐘
Learning rate decay6分鐘
The problem of local optima5分鐘
Yuanqing Lin interview13分鐘
2 個閱讀材料
Clarification about Upcoming Adam Optimization Video1分鐘
Clarification about Learning Rate Decay Video1分鐘
1 個練習
Optimization algorithms30分鐘
完成時間為 5 小時
Hyperparameter tuning, Batch Normalization and Programming Frameworks
完成時間為 5 小時
11 個視頻 （總計 104 分鐘）, 2 個閱讀材料, 2 個測驗
11 個視頻
Using an appropriate scale to pick hyperparameters8分鐘
Hyperparameters tuning in practice: Pandas vs. Caviar6分鐘
Normalizing activations in a network8分鐘
Fitting Batch Norm into a neural network12分鐘
Why does Batch Norm work?11分鐘
Batch Norm at test time5分鐘
Softmax Regression11分鐘
Training a softmax classifier10分鐘
Deep learning frameworks4分鐘
TensorFlow16分鐘
2 個閱讀材料
Clarifications about Upcoming Softmax Video1分鐘
Note about TensorFlow 1 and TensorFlow 210分鐘
1 個練習
Hyperparameter tuning, Batch Normalization, Programming Frameworks30分鐘
審閱
來自IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION的熱門評論
由 XG 提供2017年10月30日
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.
由 AM 提供2019年10月8日
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
由 YL 提供2018年2月20日
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.
由 CV 提供2017年12月23日
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 Thanks.
關於 深度学习 專項課程
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.
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