Бакалавриат
2020/2021
Нейронные сети и глубокое обучение
Статус:
Курс по выбору (Бизнес-информатика)
Направление:
38.03.05. Бизнес-информатика
Кто читает:
Департамент бизнес-информатики
Где читается:
Высшая школа бизнеса
Когда читается:
4-й курс, 2 модуль
Формат изучения:
с онлайн-курсом
Преподаватели:
Дмитриев Андрей Викторович
Язык:
английский
Кредиты:
4
Контактные часы:
2
Course Syllabus
Abstract
If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. https://www.coursera.org/learn/neural-networks-deep-learning?specialization=deep-learning#syllabus
Learning Objectives
- In this course, you will learn the foundations of deep learning. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture
Expected Learning Outcomes
- Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today.
- Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.
- Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.
- Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.
Course Contents
- Introduction to deep learningBe able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today.
- Shallow neural networksLearn to build a neural network with one hidden layer, using forward propagation and backpropagation.
- Neural Networks BasicsLearn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.
- Deep Neural NetworksUnderstand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.
Assessment Elements
- Практическое упражнение к теме №1, выполненное в ходе изучения онлайн курса на платформе Coursera
- Практическое упражнение к теме №2, выполненное при прохождении онлайн курса на платформе Coursera
- Практическое упражнение к теме №3, выполненное при прохождении онлайн курса на платформе Coursera
- Практическое упражнение к теме №4, выполненное при прохождении онлайн курса на платформе Coursera
- ЭкзаменПерезачет оценки, полученной по итогам прохождения онлайн курса на платформе Coursera. Перевод оценки в итоговую оценку в десятибалльной шкале производится путем деления оценки, полученной по итогам прохождения онлайн курса на платформе Coursera, на 10 и округления полученного результата по правилам математического округления
Interim Assessment
- Interim assessment (2 module)0.247 * Практическое упражнение к теме №1, выполненное в ходе изучения онлайн курса на платформе Coursera + 0.247 * Практическое упражнение к теме №2, выполненное при прохождении онлайн курса на платформе Coursera + 0.248 * Практическое упражнение к теме №3, выполненное при прохождении онлайн курса на платформе Coursera + 0.248 * Практическое упражнение к теме №4, выполненное при прохождении онлайн курса на платформе Coursera + 0.01 * Экзамен
Bibliography
Recommended Core Bibliography
- Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015). Weight Uncertainty in Neural Networks. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsarx&AN=edsarx.1505.05424
- Iba, H. (2018). Evolutionary Approach to Machine Learning and Deep Neural Networks : Neuro-Evolution and Gene Regulatory Networks. Singapore: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1833749
- Kim, P. (2017). MATLAB Deep Learning : With Machine Learning, Neural Networks and Artificial Intelligence. [New York, NY]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1535764
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386
Recommended Additional Bibliography
- Alexander, D. (2020). Neural Networks: History and Applications. Nova.
- Graves, A., Fernàndez, S., Gomez, F., & Schmidhuber, J. (2017). Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.163BBE7B
- Nika Sajko, Simon Kovacic, Mirko Ficko, Iztok Palcic, & Simon Klancik. (2020). Manufacturing Lead Time Prediction for Extrusion Tools With the Use of Neural Networks. Management and Production Engineering Review, 11(3), 48–55. https://doi.org/10.24425/mper.2020.134931
- Paweł Kaczmarczyk. (2020). Feedforward Neural Networks and the Forecasting of Multi-Sectional Demand for Telecom Services : a Comparative Study of Effectiveness for Hourly Data. Acta Scientiarum Polonorum. Oeconomia, 8(3), 13–25. https://doi.org/10.22630/ASPE.2020.19.3.24