课程信息

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学生职业成果

23%

完成这些课程后已开始新的职业生涯

38%

通过此课程获得实实在在的工作福利

57%

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可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
第 3 门课程(共 4 门)
可灵活调整截止日期
根据您的日程表重置截止日期。
高级
完成时间大约为22 小时
英语(English)
字幕:英语(English)

您将获得的技能

Machine LearningDeep LearningLong Short-Term Memory (ISTM)Apache Spark

学生职业成果

23%

完成这些课程后已开始新的职业生涯

38%

通过此课程获得实实在在的工作福利

57%

加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
第 3 门课程(共 4 门)
可灵活调整截止日期
根据您的日程表重置截止日期。
高级
完成时间大约为22 小时
英语(English)
字幕:英语(English)

提供方

IBM 徽标

IBM

教学大纲 - 您将从这门课程中学到什么

内容评分Thumbs Up84%(2,897 个评分)Info
1

1

完成时间为 5 小时

Introduction to deep learning

完成时间为 5 小时
16 个视频 (总计 61 分钟), 4 个阅读材料, 2 个测验
16 个视频
Introduction - Romeo Kienzler30
Introduction - Ilja Rasin1分钟
Introduction - Niketan Pansare30
Course Logistics1分钟
Cloud Architectures for AI and DeepLearning2分钟
Linear algebra6分钟
Deep feed forward neural networks12分钟
Convolutional Neural Networks4分钟
Recurrent neural networks1分钟
LSTMs3分钟
Auto encoders and representation learning2分钟
Methods for neural network training8分钟
Gradient Descent Updater Strategies6分钟
How to choose the correct activation function3分钟
The bias-variance tradeoff in deep learning3分钟
4 个阅读材料
IBM Digital Badge10分钟
Video summary on environment setup10分钟
Where to get all the code and slides for download?10分钟
Link to Github10分钟
1 个练习
DeepLearning Fundamentals14分钟
2

2

完成时间为 7 小时

DeepLearning Frameworks

完成时间为 7 小时
18 个视频 (总计 116 分钟), 1 个阅读材料, 5 个测验
18 个视频
Neural Network Debugging with TensorBoard7分钟
Automatic Differentiation2分钟
Introduction video44
Keras overview5分钟
Sequential models in keras6分钟
Feed forward networks7分钟
Recurrent neural networks9分钟
Beyond sequential models: the functional API3分钟
Saving and loading models2分钟
What is SystemML (1/2)3分钟
What is SystemML (2/2)6分钟
PyTorch Installation2分钟
PyTorch Packages2分钟
Tensor Creation and Visualization of Higher Dimensional Tensors6分钟
Math Computation and Reshape7分钟
Computation Graph, CUDA17分钟
Linear Model17分钟
1 个阅读材料
Link to files in Github10分钟
4 个练习
TensorFlow12分钟
TensorFlow 2.x12分钟
Apache SystemML12分钟
PyTorch Introduction12分钟
3

3

完成时间为 6 小时

DeepLearning Applications

完成时间为 6 小时
18 个视频 (总计 115 分钟)
18 个视频
How to implement an anomaly detector (1/2)11分钟
How to implement an anomaly detector (2/2)2分钟
How to deploy a real-time anomaly detector2分钟
Introduction to Time Series Forecasting4分钟
Stateful vs. Stateless LSTMs6分钟
Batch Size5分钟
Number of Time Steps, Epochs, Training and Validation8分钟
Trainin Set Size4分钟
Input and Output Data Construction7分钟
Designing the LSTM network in Keras10分钟
Anatomy of a LSTM Node12分钟
Number of Parameters7分钟
Training and loading a saved model4分钟
Classifying the MNIST dataset with Convolutional Neural Networks5分钟
Image classification with Imagenet and Resnet503分钟
Autoencoder - understanding Word2Vec8分钟
Text Classification with Word Embeddings4分钟
4 个练习
Anomaly Detection12分钟
Sequence Classification with Keras LSTM Network12分钟
Image Classification6分钟
NLP6分钟
4

4

完成时间为 4 小时

Scaling and Deployment

完成时间为 4 小时
3 个视频 (总计 9 分钟), 2 个阅读材料, 2 个测验
3 个视频
Computer Vision with IBM Watson Visual Recognition2分钟
Text Classification with IBM Watson Natural Language Classifier1分钟
2 个阅读材料
Exercise: Scale a Deep Learning Model on IBM Watson Machine Learning10分钟
Link to Github10分钟
1 个练习
Methods of parallel neural network training6分钟

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关于 Advanced Data Science with IBM 专项课程

As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability. If you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging....
Advanced Data Science with IBM

常见问题

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

    • 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.

  • 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.

  • 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.

  • 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.

  • The IBM Watson IoT Certified Data Scientist degree is a Coursera specialization IBM is currently creating. This course is one part of 3-4 courses coming up the next couple of months

    Currently only this and another course exist. The other one is the following:

    https://www.coursera.org/learn/exploring-visualizing-iot-data

    The course above will be modified and renamed to "Fundamentals of Applied DataScience" - but if you pass it today, it counts towards the certificate as well

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