Introduction to Deep Learning / Kurt Adrean.

By:

Adrean, Kurt

Material type: Text

TextLanguage: English Publication details: Gotland : Lynas Publishing, 2023.Description: 163 p. : ill. ; 23 cmISBN:

9789188364807

Uniform titles:

Introduction to Deep Learning

Subject(s):

DDC classification:

23 006.31 ADR

Contents:

1. License 2. Deep Learning Tutorials 3. Getting started 4. Classifying MNIST digits using Logistics Regression 5. Multiplayer Perceptron 6. Convolution Neural Networks (LeNet) 7. Denoising Autoencoders (dA) 8. Stacked Denoising Autoencoders (dA) 9. Restricted Boltzman Machine (RBM) 10. Deep Belief Networks 11. Hybrid Monte-Carlo Sampling 12. Recurrent Neural Networks with Word Embeddings 13. LSTM Networks for Sentiment Analysis 14. Modelling and Generating sequences of polyphonic music with RNN-RBM 15. Miscellaneous Index

Summary: The book guides you to deep learning through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a long time artificial intelligence researchers specializing in natural -language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamentals concepts and techniques. Students and practitioners learn the basic of deep learning by working through programs in Tensorflow, an open-source machine learning framework.

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Text Books	CUTN Central Library Generalia	Non-fiction	006.31 ADR (Browse shelf(Opens below))	Available		46856

1. License
2. Deep Learning Tutorials
3. Getting started
4. Classifying MNIST digits using Logistics Regression
5. Multiplayer Perceptron
6. Convolution Neural Networks (LeNet)
7. Denoising Autoencoders (dA)
8. Stacked Denoising Autoencoders (dA)
9. Restricted Boltzman Machine (RBM)
10. Deep Belief Networks
11. Hybrid Monte-Carlo Sampling
12. Recurrent Neural Networks with Word Embeddings
13. LSTM Networks for Sentiment Analysis
14. Modelling and Generating sequences of polyphonic music with RNN-RBM
15. Miscellaneous
Index

The book guides you to deep learning through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a long time artificial intelligence researchers specializing in natural -language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamentals concepts and techniques. Students and practitioners learn the basic of deep learning by working through programs in Tensorflow, an open-source machine learning framework.

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