The application of machine learning is deepening and widening in the world of business and technology. Among different machine learning algorithms, deep learning stands out. Since its introduction in 2006, deep learning has fulfilled some of the oldest artificial intelligence promises, such as autonomous/driverless vehicles, machine translation, precise and speaker-independent speech recognition, and robust visual object recognition. Deep learning systems have even beaten the human experts in their field and achieved remarkable performances.
In this course, we will address the what, why and how of deep learning. What is deep learning? Why do we need deep learning? And how do we apply and harness the benefits of deep learning in business cases? We will focus on the three popular deep learning algorithms, namely Convolutional Neural Networks (CNN), Long/Short Term Memories (LSTM), and Generative Adversarial Networks (GAN), and their applications. The methods and platforms for implementation and evaluation of deep learning systems would be discussed. Furthermore, learners will practise employing deep learning to deal with a few applied examples using Python and Octave environments.
At the end of the course, participants will be able to:
- Articulate an efficient procedure of implementation and evaluation of deep learning models.
- Understand the basic definitions and applications of CNN, LSTM, and GAN.
- Define the business case for a deep learning approach.
- Demonstrate an understanding of the practical aspects of deep learning, its platforms and tools.
- Solve authentic business problems with deep learning models.
- Getting familiar with the course
- A review on AI and Machine Learning
- Deep Learning basic terms and definitions
- Deep Learning applications
- Deep Learning models
- Convolutional Neural Networks
- Long/Short Term Memories
- Generative Adversarial Networks
- Deep Learning platforms and environments
- Python and TensorFlow example
- Octave example
- Solving authentic business problems with Deep Learning models
- Deep Learning models performance measurement and evaluation
Basic knowledge of machine learning and Python programming
Who Should Attend
IT Engineers, IT Consultants, IT Managers, Technology Managers, Business Managers
Mode of Training
Dr. Amirhassan Monajemi
Amirhassan Monajemi is a Senior Lecturer
in AI and Data Science with the School of Continuing and Lifelong Education
(SCALE) at the National University of Singapore (NUS). Before joining the NUS,
he was with the Faculty of Computer Engineering, University of Isfahan, Iran, where
he was serving as a professor of AI, Machine Learning, and Data Science. He was
born in 1968 in Isfahan, Iran. He studied towards BSc and MSc in Computer
Engineering at Isfahan University of Technology (IUT), and Shiraz University
respectively. He got his PhD in computer engineering, pattern recognition and
image processing, from the University of Bristol, Bristol, England, in 2005.
His research interests include AI, Machine Learning, Machine Vision, IoT, Data
Science, and their applications.
He has taught the artificial intelligence courses, including AI,
Advanced AI, Expert Systems, Decision Support Systems, Neural Networks, and
Cognitive Science since 2005 at both undergraduate and postgraduate levels. He
was awarded the best university teacher of the province in 2012. He also has
studied Learning Management Systems, E-Learning, and E-Learning for workplaces
Dr. Monajemi has registered a few patents in the fields of AI,
Machine Vision, and Signal Processing applications, including an AI and machine
vision-based driver drowsiness detection system and a low power consuming
spherical robot. He also has published more than a hundred research papers in
peer-reviewed, indexed journals and international conferences (IEEE, Elsevier,
Springer, and so on), and supervised several Data Science, IoT, and AI
industrial projects in various scales, including Isfahan intelligent traffic
system delivery and testing, and red light runners detection. He is experienced
in different sub-domains of Artificial Intelligence and Machine Learning, from
theory to practice, including Deep Learning, Logic, and Optimization.