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.
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習得するスキル
- Deep Learning
- Artificial Neural Network
- Machine Learning
- Reinforcement Learning
- keras
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IBM
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.
シラバス - 本コースの学習内容
Introduction to Neural Networks
This module introduces Deep Learning, Neural Networks, and their applications. You will go through the theoretical background and characteristics that they share with other machine learning algorithms, as well as characteristics that makes them stand out as great modeling techniques for specific scenarios. You will also gain some hands-on practice on Neural Networks and key concepts that help these algorithms converge to robust solutions.
Neural Network Optimizers and Keras
You can leverage several options to prioritize the training time or the accuracy of your neural network and deep learning models. In this module you learn about key concepts that intervene during model training, including optimizers and data shuffling. You will also gain hands-on practice using Keras, one of the go-to libraries for deep learning.
Convolutional Neural Networks
In this module you become familiar with convolutional neural networks, also known as space invariant artificial neural networks, a type of deep neural networks, frequently used in image AI applications. There are several CNN architectures, you will learn some of the most common ones to add to your toolkit of Deep Learning Techniques.
Recurrent Neural Networks and Long-Short Term Memory Networks
In this module you become familiar with Recursive Neural Networks (RNNs) and Long-Short Term Memory Networks (LSTM), a type of RNN considered the breakthrough for speech to text recongintion. RNNs are frequently used in most AI applications today, and can also be used for supervised learning.
レビュー
DEEP LEARNING AND REINFORCEMENT LEARNING からの人気レビュー
The concepts were clearly explained in lectures. The assignments were very helpful to gain a practical insight of the skills learned in the course.
Excellent from the theory and the practice! Great explainatory videos and detailed jupyter notebooks!
Hello, thank you again for the course. My congrats, once more, to the instructor on the videos!
The difficult terms are simplified enough for understanding and application in real life.
IBM機械学習 プロフェッショナル認定について
Machine Learning is one of the most in-demand skills for jobs related to modern AI applications, a field in which hiring has grown 74% annually for the last four years (LinkedIn). This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning. It also complements your learning with special topics, including Time Series Analysis and Survival Analysis.
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