This book covers both classical and modern models in deep learning. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on under
Neural networks and deep learning: a textbook
โ Scribed by Aggarwal, Charu C
- Publisher
- Springer
- Year
- 2018
- Tongue
- English
- Leaves
- 512
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
Introduction to Neural Networks -- 2 Machine Learning with Shallow Neural Networks -- Training Deep Neural Networks -- Teaching Deep Learners to Generalize -- Radical Basis Function Networks -- Restricted Boltzmann Machines -- Recurrent Neural Networks -- Convolutional Neural Networks -- Deep Reinforcement Learning -- Advanced Topics in Deep Learning.
โฆ Table of Contents
Introduction to Neural Networks --
2 Machine Learning with Shallow Neural Networks --
Training Deep Neural Networks --
Teaching Deep Learners to Generalize --
Radical Basis Function Networks --
Restricted Boltzmann Machines --
Recurrent Neural Networks --
Convolutional Neural Networks --
Deep Reinforcement Learning --
Advanced Topics in Deep Learning.
โฆ Subjects
Machine learning;Neural networks (Computer science);Redes neuronales (Informatica)
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This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concep
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