Neural Networks and Deep Learning
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Artificial neural networks are models of reasoning based on human brain functioning and have been successful in many real-world applications including pattern classification, regression, forecasting, etc. The course will introduce models, learning, implementations and applications of neural networks and deep learning.
To equip participants with the basic concepts and methodologies of neural networks and deep learning systems. In particular, this course covers the information processing techniques inspired by the workings of biological neural networks, which provides solution to interrogatives that current linear systems are not able to resolve. Basic neuron models, neural layers, feedforward networks, convolutional neural networks, autoencoders, and recurrent neural networks will be covered in the course. Students will be given hands-on experience in building neural network models, using Python and Tensorflow libraries. After taking this course, from shallow to deep neural networks, students will be able to design and select suitable neural network model for solving real world applications and perform required simulations and implementations.
- Introduction to neural networks
- Pattern recognition
- Implementing neural networks, using Python and Theano
- Neural layers
- Feedforward neural networks
- Model selection and overfitting
- Convolutional neural networks
- Recurrent neural networks
- Gated RNN
Who should attend
Technicians, engineers, data modelers, and computational scientists who are interested in developing neural network models to solve computational problems, including pattern/object recognition, regression, prediction, forecasting, etc. Knowledge of linear algebra, calculus, basic programming skills, and Python would be useful.