课程信息
4.4
171 个评分
36 个审阅
专项课程

第 3 门课程(共 4 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
高级

高级

完成时间(小时)

完成时间大约为21 小时

建议:4 weeks of study, 4-6 hours/week...
可选语言

英语(English)

字幕:英语(English)

您将获得的技能

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

第 3 门课程(共 4 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
高级

高级

完成时间(小时)

完成时间大约为21 小时

建议:4 weeks of study, 4-6 hours/week...
可选语言

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 5 小时

Introduction to deep learning

...
Reading
17 个视频 (总计 65 分钟), 5 个阅读材料, 2 个测验
Video17 个视频
Introduction - Romeo Kienzler30s
Introduction - Ilja Rasin1分钟
Introduction - Niketan Pansare30s
Introduction - Tom Hanlon1分钟
Course Logistics1分钟
Cloud Architectures for AI and DeepLearning4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
Reading5 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
Introduction to ApacheSpark (optional)10分钟
Link to Github10分钟
Quiz1 个练习
DeepLearning Fundamentals14分钟
2
完成时间(小时)
完成时间为 7 小时

deep learning frameworks

...
Reading
24 个视频 (总计 168 分钟), 1 个阅读材料, 5 个测验
Video24 个视频
Neural Network Debugging with TensorBoard7分钟
Automatic Differentiation2分钟
Introduction video44s
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
Demo - How to use Apache SystemML on IBM DSX (1/3)4分钟
Demo - How to use Apache SystemML on IBM DSX (2/3)3分钟
Demo - How to use Apache SystemML on IBM DSX (3/3)8分钟
Introduction to DeepLearning4J12分钟
Demo: Running Java in Data Science Experience8分钟
DL4J Neural Network Code Example, Mnist Classifier14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
Reading1 个阅读材料
Link to files in Github10分钟
Quiz4 个练习
TensorFlow12分钟
Apache SystemML12分钟
DL4J Fundamentals12分钟
PyTorch Introduction12分钟
3
完成时间(小时)
完成时间为 6 小时

DeepLearning Applications

...
Reading
18 个视频 (总计 115 分钟), 1 个阅读材料, 5 个测验
Video18 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
Reading1 个阅读材料
Link to Github10分钟
Quiz4 个练习
Anomaly Detection12分钟
Sequence Classification with Keras LSTM Network12分钟
Image Classification6分钟
NLP6分钟
4
完成时间(小时)
完成时间为 4 小时

scaling and deployment

...
Reading
6 个视频 (总计 47 分钟), 2 个阅读材料, 2 个测验
Video6 个视频
Creating and Scaling a Keras Model in ApacheSpark using DL4J14分钟
Creating and Scaling a Keras Model in ApacheSpark using DL4J (Demo)16分钟
Scale TensorFlow with IBM Watson Machine Learning6分钟
Computer Vision with IBM Watson Visual Recognition2分钟
Text Classification with IBM Watson Natural Language Classifier1分钟
Reading2 个阅读材料
Parallel Neural Network Training10分钟
Link to Github10分钟
Quiz1 个练习
Run a Notebook using Keras and DL4J6分钟
4.4
36 个审阅Chevron Right

热门审阅

创建者 RCApr 26th 2018

It was really great learning with coursera and I loved the course. The way faculty teaches here is just awesome as they are very much clear and helped a lot while learning this coursea

创建者 MKApr 16th 2018

Useful information course have. There are some challenges.\n\nHowever, the instructor, Romeo is great!\n\nA real Jedi master!

讲师

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Romeo Kienzler

Chief Data Scientist, Course Lead
IBM Watson IoT
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Niketan Pansare

Senior Software Engineer
IBM Research
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Tom Hanlon

Training Director
Skymind
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Max Pumperla

Deep Learning Engineer
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Ilja Rasin

Data Scientist
IBM Watson Health

关于 IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

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

  • 注册以便获得证书后,您将有权访问所有视频、测验和编程作业(如果适用)。只有在您的班次开课之后,才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程,可能无法访问某些作业。

  • 您注册课程后,将有权访问专项课程中的所有课程,并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中,您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容,可以免费旁听课程。

  • 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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