Computational Methods for Deep Learning
Theory, Algorithms, and Implementations
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16 Result(s)
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Wei Qi Yan
English
Artificial Intelligence
Mathematics of Computing
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Chapter
Transfer Learning and Ensemble Learning
In this chapter, we start from transfer learning and introduce the relationship between learners. We use ensemble learning to combine them together and hope to get a strong learner from a weak learner by chang...
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Chapter
Deep Learning Platforms
There are a plethora of deep learning platforms available at present. The famous one is MATLAB deep learning toolbox developed by MathWorks which simplifies deep learning computations and reduces the workload ...
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Chapter
Generative Adversarial Networks and Siamese Nets
In this chapter, we will emphasize computational iterations in GANs (i.e., generative adversarial networks) [46] and Siamese nets [3, 6, 15] . In deep learning, these models are named as contrastive networks [3]...
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Chapter
Manifold Learning and Graph Neural Network
In this chapter, we will introduce manifold learning and graph neural networks. We hope to introduce graphical probability models as the starting point of basestone. We need to introduce our readers why we sho...
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Book
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Chapter
Introduction
This chapter covers the fundamentals of deep learning, therefore, we present relevant knowledge in chronological order so as to fully introduce the history of deep learning development; meanwhile, we review ho...
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Chapter
Convolutional Neural Networks and Recurrent Neural Networks
In this chapter, we will introduce the typical deep neural networks from the viewpoint of Convolutional Neural Network (CNN or ConvNet) family, especially , Single Shot MultiBox Detector (SSD) , and You Only...
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Chapter
Reinforcement Learning
In this chapter, we introduce fundamental concepts of reinforcement learning [21] such as , , deep Q- , and double Q- . We detail why reinforcement is thought as a method of deep learning.
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Chapter
CapsNet and Manifold Learning
is one of the relatively new methods in deep learning, which has taken topological of a scene into consideration. The output will be a vector to reflect this relationship. Meanwhile, manifold , which is em...
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Chapter
Transfer Learning and Ensemble Learning
In this chapter, we start from transfer learning and introduce the relationship between different learners; we use ensemble learning to combine them together and hope to get a strong learner from a weak learne...
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Chapter
Reinforcement Learning
In this chapter, we will introduce the fundamental concepts of reinforcement learning such as , , deep Q- , etc. We will introduce why reinforcement is thought as a method of deep learning. Then, mathemati...
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Chapter
Boltzmann Machines
In this chapter, we will introduce , restricted Boltzmann , and deep Boltzmann . We will generalize our deep neural networks from networks to general graphs, we will use probabilistic graphical to model t...
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Chapter
Deep Learning Platforms
There are many deep learning platforms available such as , , MXNet, , and Theano. Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, which originally was developed a...
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Chapter
Autoencoder and GAN
In this chapter, we will emphasize on computational iterations in autoencoder and GAN (generative adversarial learning). Additionally, we will deeply learn deep learning and interpret how information theory ha...
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Chapter
Introduction
This chapter covers the fundamentals of deep learning, we present the relevant knowledge in chronological order so as to fully introduce the history and development of deep learning; meanwhile, we review how t...
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Chapter
CNN and RNN
In this chapter, we will introduce the typical deep neural networks from the viewpoint of family, especially region-based , , and . Meanwhile, from the viewpoint of time series analysis, we depict the f...
