Java Deep Learning Essentials : dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with Java /
Dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with JavaAbout This Book Go beyond the theory and put Deep Learning into practice with Java Find out how to build a range of Deep Learning algorithms using a range of...
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| 格式: | 图书 |
| 语言: | English |
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Birmingham ; Mumbai :
Packt Publishing,
c2016
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| 丛编: | Community experience distilled
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| Classic Catalogue: | View this record in Classic Catalogue |
书本目录:
- Chapter 1: Deep Learning Overview; Transition of AI; Definition of AI; AI booms in the past; Machine learning evolves; What even machine learning cannot do; Things dividing a machine and human; AI and deep learning; Summary; Chapter 2: Algorithms for Machine Learning
- Preparing for Deep Learning; Getting started; The need for training in machine learning; Supervised and unsupervised learning; Support Vector Machine (SVM); Hidden Markov Model (HMM); Neural networks. Logistic regressionReinforcement learning; Machine learning application flow; Theories and algorithms of neural networks; Perceptrons (single-layer neural networks); Logistic regression; Multi-class logistic regression; Multi-layer perceptrons (multi-layer neural networks); Summary; Chapter 3: Deep Belief Nets and Stacked Denoising Autoencoders; Neural networks fall; Neural networks' revenge; Deep learning's evolution
- what was the breakthrough?; Deep learning with pre-training; Deep learning algorithms; Restricted Boltzmann machines; Deep Belief Nets (DBNs); Denoising Autoencoders. Stacked Denoising Autoencoders (SDA)Summary; Chapter 4: Dropout and Convolutional Neural Networks; Deep learning algorithms without pre-training; Dropout; Convolutional neural networks; Convolution; Pooling; Equations and implementations; Summary; Chapter 5: Exploring Java Deep Learning Libraries
- DL4J, ND4J, and More; Implementing from scratch versus a library/framework; Introducing DL4J and ND4J; Implementations with ND4J; Implementations with DL4J; Setup; Build; DBNIrisExample.java; CSVExample.java; CNNMnistExample.java/LenetMnistExample.java; Learning rate optimization; Summary. Chapter 6: Approaches to Practical Applications
- Recurrent Neural Networks and MoreFields where deep learning is active; Image recognition; Natural language processing; Feed-forward neural networks for NLP; Deep learning for NLP; The difficulties of deep learning; The approaches to maximizing deep learning possibilities and abilities; Field-oriented approach; Medicine; Automobiles; Advert technologies; Profession or practice; Sports; Breakdown-oriented approach; Output-oriented approach; Summary; Chapter 7: Other Important Deep Learning Libraries; Theano; TensorFlow; Caffe; Summary. Chapter 8: What's Next?Breaking news about deep learning; Expected next actions; Useful news sources for deep learning; Summary; Index.