Bài giảng "Máy học nâng cao: Deep learning - An introduction" cung cấp cho người đọc các nội dung: Introduction, applications, convolutional neural networks vs. 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 (2024)
- 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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