Artificial Neural Networks and Deep Learning in Practice
What you'll learn
- The fundamentals of Artificial Neural Networks (ANNs) and reviews state-of-the-art DL examples.
- The fundamental of Deep learning and the most popular algorithms.
- It has several coding examples.
- It has several quizzes for learning better.
Requirements
- Probability,
- Calculus,
- Basic of Python, Tensor Flow, Keras, and Numpy.
Description
Artificial Neural Networks and Deep Learning are the most recent and advanced topics in machine learning, with several applications in many fields. They show promising results in many areas, from computer vision to drug discovery and stock market prediction. Also, because of its capabilities and potential in solving different problems by deploying different data types, many researchers and people who are not in computer science or related fields are interested in learning and using Artificial Neural Networks and Deep learning architectures in their projects.
This course gives you some fundamentals of artificial neural networks and deep learning with some coding examples to understand the concepts better. The course is suitable for people who are new in the machine learning field and deep learning and would like to learn how to implement deep learning algorithms using Python, TensorFlow, and Keras.
The course provides some references and links for more reading. You have access to the Q/A session for asking your question. You also have access to communicate with the professor by the messaging system to ask your questions.
I would expect this course’s contents to be welcomed worldwide by undergraduate and graduate students and researchers in deep learning, including practitioners in academia and industry.
Who this course is for:
- It is useful for undergraduate and graduate students, as well as practitioners in industry and academia.
- Anyone who would like to learn Neural networks and deep learning.
Instructor
- -- Instructor Rating
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- 19 Students
- 2 Courses
Dr. Mehdi Ghayoumi is a course facilitator at Cornell University and adjunct faculty of Computer Science at the University of San Diego. Prior to this, he was a research assistant professor at SUNY at Binghamton, where he was the Media Core Lab’s dynamic leader. He was also a lecturer at Kent State University, where he received the Teaching Award for two consecutive years in 2016 and 2017. In addition, he has been teaching machine learning, data science, robotic and programming courses for several years.
Dr. Ghayoumi research interests are in Machine Learning, Machine Vision, Robotics, and Human-Robot Interaction (HRI). His research focuses are on building real systems for realistic environment settings, and his current projects have applications in Human-Robot Interaction, manufacturing, biometric, and healthcare.
He is a technical program committee member of several conferences, workshops, and editorial board member of several journals in machine learning, mathematics, and robotics, like ICML, ICPR, HRI, FG, WACV, IROS, CIBCB, and JAI. In addition, his research papers have been published at conferences and journals in the fields, including Human-Computer Interaction (HRI), Robotics Science and Systems (RSS), International Conference on Machine Learning and Applications (ICMLA), and others.