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Deep Learning and Reinforcement Learning
AWS Training Pass
Take advantage of flexible training options with the AWS Training Pass and get Authorized AWS Training for a full year.
This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future. After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning.
IBM Customers and Sellers: If you are interested in this course, consider purchasing it as part of one of these Individual or Enterprise Subscriptions:
- IBM Learning for Data and AI Individual Subscription (SUBR022G)
- IBM Learning for Data and AI Enterprise Subscription (SUBR004G)
- IBM Learning Individual Subscription with Red Hat Learning Services (SUBR023G)
By the end of this course you should be able to:- Explain the kinds of problems suitable for Unsupervised Learning approaches.
- Explain the curse of dimensionality, and how it makes clustering difficult with many features.
- Describe and use common clustering and dimensionality-reduction algorithms.
- Try clustering points where appropriate, compare the performance of per-cluster models.
- Understand metrics relevant for characterizing clusters
This course targets aspiring data scientists interested in acquiring hands-on experience with Deep Learning and Reinforcement Learning.
To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Calculus, Linear Algebra, Probability, and Statistics.
1. Introduction to Neural Networks
2. Neural Network Optimizers and Keras
3. Convolutional Neural Networks
4. Recurrent Neural Networks and Long-Short Term Memory Networks
5. Deep Learning with Autoencoders
6. Deep Learning Applications and Reinforcement Learning
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
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Self-Paced Training Terms & Conditions
IBM Web Based Training courses are sold on a per-user basis. WBT courses provide a training advantage for you and your teams, helping you get up to speed quickly. Take the courses you need, at your convenience and at your own pace.
You can start the course at any time within 12 months of enrolling for the course. After you register/start the course, you have 30 days to complete your course. Within this 30 days, the self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.
System RequirementsTo participate in this course, the student workstation must meet the following hardware requirements:
- Minimum of 256 MB of memory
- Windows Vista or Windows 7-10 (32-bit or 64-bit edition)
- Internet Explorer 6 or higher or Firefox ESR
- 128-bit encryption
- Citrix Receiver for connection to the IBM Remote Lab Platform
- Access to Internet with at least 128 kbps down and 128 kbps up
- Other platforms/combinations may work but are not officially supported by IBM.
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