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Bài giảng Máy học nâng cao: Deep learning an introduction - Trịnh Tấn Đạt

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Bài giảng "Máy học nâng cao: Deep learning an introduction" cung cấp cho người học các kiến thức: Introduction, applications, convolutional neural networks and recurrent neural networks, hardware and software. Mời các bạn cùng tham khảo nội dung chi tiết.

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Nội dung Text: Bài giảng Máy học nâng cao: Deep learning an introduction - Trịnh Tấn Đạt

Trịnh Tấn Đạt Khoa CNTT – Đại Học Sài Gòn Email: trinhtandat@sgu.edu.vn Website: https://sites.google.com/site/ttdat88/
Contents  Introduction  Applications  Convolutional Neural Networks vs. Recurrent Neural Networks  Hardware and Software
Introduction to Deep Learning
Introduction to Deep Learning
Why Deep Learning?  Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed  Methods that can learn from and make predictions on data
Why Deep Learning?
Why Deep Learning?
Why Deep Learning?  Can we learn the underlying features directly from data?
Why Deep Learning?  ML vs. Deep Learning:  Most machine learning methods work well because of human-designed representations and input features ML becomes just optimizing weights to best make a final prediction
Why Deep Learning?  Challenges of ML:  Relevant data acquisition  Data preprocessing  Feature selection  Model selection: simplicity versus complexity  Result interpretation.
What is Deep Learning (DL)?  A machine learning subfield of learning representations of data. Exceptional effective at learning patterns.  Deep learning algorithms attempt to learn (multiple levels of) representation by using a hierarchy of multiple layers  If you provide the system tons of information, it begins to understand it and respond in useful ways.
Why is DL useful?  Manually designed features are often over-specified, incomplete and take a long time to design and validate  Learned Features are easy to adapt, fast to learn  Deep learning provides a very flexible, (almost?) universal, learnable framework for representing world, visual and linguistic information.  Can learn both unsupervised and supervised  Utilize large amounts of training data In ~2010 DL started outperforming other ML techniques first in speech and vision, then NLP
Why is DL useful?
Why is DL useful?
Why Now?
The Perceptron: Forward Propagation  Neural Network Architectures  Back Propagation for Weight Update
Importance of Activation Functions  The purpose of activation functions is to introduce non-linearities into the network
Introduction to Deep Learning  Activation function
Introduction to Deep Learning  Neural Network Adjustements

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