Computational Methods for Deep Learning
Theory, Algorithms, and Implementations
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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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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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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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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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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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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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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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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...
Chapter
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...
Chapter
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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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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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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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...
Chapter
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...
Chapter
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...