This course starts with a gentle introduction from machine learning using conventional neural networks to constructing powerful, state-of-the-art deep neural network models for sophisticated tasks, such as natural language processing, speech recognition, and object detection and recognition.
- Introduction to machine learning
- Regression and perceptron
- Conventional neural network
- Forward and backward propagation
- Deep neural network basics
- Convolutional neural network
- CNN and object detection and recognition
- Deep convolution models
- Deep convolution example applications
- Recurrent neural networks
- RNN and natural language processing
- RNN and speech recognition
- RNN and attention
All topics are facilitated with lab materials. Programming language:
Introduction to Python programming is also provided in the course lab sessions.
This is a more advanced course on machine learning. Fundamental knowledge on basic machine learning techniques and some experience in the related area are desirable. An introduction to machine learning will be provided but the emphasis of this workshop is on deep learning. A brief introduction to Python will also be provided; it is however assumed that delegates have some previous programming experience. If you are relatively new to the field, you may be interested in the workshop on Introduction to Machine Learning, which provides a comprehensive introduction to the field with abundant practical examples in Python and Matlab.