About this Course
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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

다음 전문 분야의 4개 강좌 중 3번째 강좌:

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 18시간 필요

권장: 4 weeks of study, 4-6 hours/week...

영어

자막: 영어

귀하가 습득할 기술

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

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

다음 전문 분야의 4개 강좌 중 3번째 강좌:

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 18시간 필요

권장: 4 weeks of study, 4-6 hours/week...

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 5시간 필요

Introduction to deep learning

17개 동영상 (총 65분), 6 readings, 2 quizzes
17개의 동영상
Introduction - Romeo Kienzler30
Introduction - Ilja Rasin1m
Introduction - Niketan Pansare30
Introduction - Tom Hanlon1m
Course Logistics1m
Cloud Architectures for AI and DeepLearning4m
Linear algebra6m
Deep feed forward neural networks12m
Convolutional Neural Networks4m
Recurrent neural networks1m
LSTMs3m
Auto encoders and representation learning2m
Methods for neural network training8m
Gradient Descent Updater Strategies6m
How to choose the correct activation function3m
The bias-variance tradeoff in deep learning3m
6개의 읽기 자료
IBM Digital Badge10m
Video summary on environment setup10m
Where to get all the code and slides for download?10m
IMPORTANT: How to submit your programming assignments10m
Introduction to ApacheSpark10m
Link to Github10m
1개 연습문제
DeepLearning Fundamentals14m
2
완료하는 데 7시간 필요

DeepLearning Frameworks

24개 동영상 (총 168분), 1 reading, 5 quizzes
24개의 동영상
Neural Network Debugging with TensorBoard7m
Automatic Differentiation2m
Introduction video44
Keras overview5m
Sequential models in keras6m
Feed forward networks7m
Recurrent neural networks9m
Beyond sequential models: the functional API3m
Saving and loading models2m
What is SystemML (1/2) ?3m
What is SystemML (2/2) ?6m
Demo - How to use Apache SystemML on IBM DSX (1/3)4m
Demo - How to use Apache SystemML on IBM DSX (2/3)3m
Demo - How to use Apache SystemML on IBM DSX (3/3)8m
Introduction to DeepLearning4J12m
Demo: Running Java in Data Science Experience8m
DL4J Neural Network Code Example, Mnist Classifier14m
PyTorch Installation2m
PyTorch Packages2m
Tensor Creation and Visualization of Higher Dimensional Tensors6m
Math Computation and Reshape7m
Computation Graph, CUDA17m
Linear Model17m
1개의 읽기 자료
Link to files in Github10m
4개 연습문제
TensorFlow12m
Apache SystemML12m
DL4J Fundamentals12m
PyTorch Introduction12m
3
완료하는 데 6시간 필요

DeepLearning Applications

18개 동영상 (총 115분), 1 reading, 5 quizzes
18개의 동영상
How to implement an anomaly detector (1/2)11m
How to implement an anomaly detector (2/2)2m
How to deploy a real-time anomaly detector2m
Introduction to Time Series Forecasting4m
Stateful vs. Stateless LSTMs6m
Batch Size5m
Number of Time Steps, Epochs, Training and Validation8m
Trainin Set Size4m
Input and Output Data Construction7m
Designing the LSTM network in Keras10m
Anatomy of a LSTM Node12m
Number of Parameters7m
Training and loading a saved model4m
Classifying the MNIST dataset with Convolutional Neural Networks5m
Image classification with Imagenet and Resnet503m
Autoencoder - understanding Word2Vec8m
Text Classification with Word Embeddings4m
1개의 읽기 자료
Generative Adversarial Networks (GANs) (optional)10m
4개 연습문제
Anomaly Detection12m
Sequence Classification with Keras LSTM Network12m
Image Classification6m
NLP6m
4
완료하는 데 4시간 필요

Scaling and Deployment

5개 동영상 (총 40분), 3 readings, 2 quizzes
5개의 동영상
Creating and Scaling a Keras Model in ApacheSpark using DL4J14m
Creating and Scaling a Keras Model in ApacheSpark using DL4J (Demo)16m
Computer Vision with IBM Watson Visual Recognition2m
Text Classification with IBM Watson Natural Language Classifier1m
3개의 읽기 자료
Parallel Neural Network Training10m
Scale a Keras Model with IBM Watson Machine Learning10m
Link to Github10m
1개 연습문제
Run a Notebook using Keras and DL4J6m
4.5
88개의 리뷰Chevron Right

23%

이 강좌를 수료한 후 새로운 경력 시작하기

38%

이 강좌를 통해 확실한 경력상 이점 얻기

57%

급여 인상 또는 승진하기

Applied AI with DeepLearning의 최상위 리뷰

대학: 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

대학: BSAug 8th 2019

Gave a good hands-on with IBM Watson studio notebooks. Also a good overview of LSTM's, Keras, Predictive maintenance. Good stuff, keep it going

강사

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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

IBM 정보

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....

Advanced Data Science with IBM 전문 분야 정보

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

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

  • 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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