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.
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
- 5 stars74.28%
- 4 stars16.19%
- 3 stars6.66%
- 2 stars1.90%
- 1 star0.95%
来自DEEP LEARNING AND REINFORCEMENT LEARNING的热门评论
The concepts were clearly explained in lectures. The assignments were very helpful to gain a practical insight of the skills learned in the course.
Reinforcement Learning part needs to be a separate course and more details in it
Very good. I learned a lot but the subject matter is quite extensive.
The difficult terms are simplified enough for understanding and application in real life.