Об этом курсе
4.4
Оценки: 148
Рецензии: 35
>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area <<< This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. We’ll learn about the fundamentals of Linear Algebra and Neural Networks. Then we introduce the most popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras one real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs. IMPORTANT: THIS COURSE ALONE IS NOT SUFFICIENT TO OBTAIN THE "IBM Watson IoT Certified Data Scientist certificate". You need to take three other courses where two of them are currently built. The Specialization will be ready late spring, early summer 2018 Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give your ideas for turbocharging successful creation and deployment of DeepLearning models. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you’ve ever wanted to become better at anything, this course will help serve as your guide. Prerequisites: Some coding skills are necessary. Preferably python, but any other programming language will do fine. Also some basic understanding of math (linear algebra) is a plus, but we will cover that part in the first week as well. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging....
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Advanced Level

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Approx. 19 hours to complete

Предполагаемая нагрузка: 4 weeks of study, 4-6 hours/week...
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Приобретаемые навыки

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

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.
Calendar

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.
Advanced Level

Продвинутый уровень

Clock

Approx. 19 hours to complete

Предполагаемая нагрузка: 4 weeks of study, 4-6 hours/week...
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English

Субтитры: English...

Программа курса: что вы изучите

Week
1
Clock
5 ч. на завершение

Introduction to deep learning

...
Reading
17 видео (всего 65 мин.), 5 материалов для самостоятельного изучения, 2 тестов
Video17 видео
Introduction - Romeo Kienzlerмин
Introduction - Ilja Rasin1мин
Introduction - Niketan Pansareмин
Introduction - Tom Hanlon1мин
Course Logistics1мин
Cloud Architectures for AI and DeepLearning4мин
Linear algebra6мин
Deep feed forward neural networks12мин
Convolutional Neural Networks4мин
Recurrent neural networks1мин
LSTMs3мин
Auto encoders and representation learning2мин
Methods for neural network training8мин
Gradient Descent Updater Strategies6мин
How to choose the correct activation function3мин
The bias-variance tradeoff in deep learning3мин
Reading5 материала для самостоятельного изучения
IBM Digital Badge10мин
Video summary on environment setup10мин
Where to get all the code and slides for download?10мин
Introduction to ApacheSpark (optional)10мин
Link to Github10мин
Quiz1 практическое упражнение
DeepLearning Fundamentals14мин
Week
2
Clock
7 ч. на завершение

deep learning frameworks

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Reading
24 видео (всего 168 мин.), 1 материал для самостоятельного изучения, 5 тестов
Video24 видео
Neural Network Debugging with TensorBoard7мин
Automatic Differentiation2мин
Introduction videoмин
Keras overview5мин
Sequential models in keras6мин
Feed forward networks7мин
Recurrent neural networks9мин
Beyond sequential models: the functional API3мин
Saving and loading models2мин
What is SystemML (1/2) ?3мин
What is SystemML (2/2) ?6мин
Demo - How to use Apache SystemML on IBM DSX (1/3)4мин
Demo - How to use Apache SystemML on IBM DSX (2/3)3мин
Demo - How to use Apache SystemML on IBM DSX (3/3)8мин
Introduction to DeepLearning4J12мин
Demo: Running Java in Data Science Experience8мин
DL4J Neural Network Code Example, Mnist Classifier14мин
PyTorch Installation2мин
PyTorch Packages2мин
Tensor Creation and Visualization of Higher Dimensional Tensors6мин
Math Computation and Reshape7мин
Computation Graph, CUDA17мин
Linear Model17мин
Reading1 материал для самостоятельного изучения
Link to files in Github10мин
Quiz4 практического упражнения
TensorFlow12мин
Apache SystemML12мин
DL4J Fundamentals12мин
PyTorch Introduction12мин
Week
3
Clock
6 ч. на завершение

DeepLearning Applications

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Reading
18 видео (всего 115 мин.), 1 материал для самостоятельного изучения, 5 тестов
Video18 видео
How to implement an anomaly detector (1/2)11мин
How to implement an anomaly detector (2/2)2мин
How to deploy a real-time anomaly detector2мин
Introduction to Time Series Forecasting4мин
Stateful vs. Stateless LSTMs6мин
Batch Size5мин
Number of Time Steps, Epochs, Training and Validation8мин
Trainin Set Size4мин
Input and Output Data Construction7мин
Designing the LSTM network in Keras10мин
Anatomy of a LSTM Node12мин
Number of Parameters7мин
Training and loading a saved model4мин
Classifying the MNIST dataset with Convolutional Neural Networks5мин
Image classification with Imagenet and Resnet503мин
Autoencoder - understanding Word2Vec8мин
Text Classification with Word Embeddings4мин
Reading1 материал для самостоятельного изучения
Link to Github10мин
Quiz4 практического упражнения
Anomaly Detection12мин
Sequence Classification with Keras LSTM Network12мин
Image Classification6мин
NLP6мин
Week
4
Clock
4 ч. на завершение

scaling and deployment

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Reading
6 видео (всего 47 мин.), 2 материалов для самостоятельного изучения, 2 тестов
Video6 видео
Creating and Scaling a Keras Model in ApacheSpark using DL4J14мин
Creating and Scaling a Keras Model in ApacheSpark using DL4J (Demo)16мин
Scale TensorFlow with IBM Watson Machine Learning6мин
Computer Vision with IBM Watson Visual Recognition2мин
Text Classification with IBM Watson Natural Language Classifier1мин
Reading2 материала для самостоятельного изучения
Parallel Neural Network Training10мин
Link to Github10мин
Quiz1 практическое упражнение
Run a Notebook using Keras and DL4J6мин
4.4

Лучшие рецензии

автор: 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

автор: FWMay 18th 2018

Good connection from the theory in Standford University: "Machine Learning" to modern day implementations of ML.

Преподавателя

Romeo Kienzler

Chief Data Scientist, Course Lead
IBM Watson IoT

Niketan Pansare

Senior Software Engineer
IBM Research

Tom Hanlon

Training Director
Skymind

Max Pumperla

Deep Learning Engineer

Ilja Rasin

Data Scientist
IBM Watson Health

О 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....

О специализации ''Advanced Data Science with IBM'

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

Часто задаваемые вопросы

  • 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.

  • 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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