MARC details
000 -LEADER |
fixed length control field |
01994nam a22002777a 4500 |
003 - CONTROL NUMBER IDENTIFIER |
control field |
CUTN |
005 - DATE AND TIME OF LATEST TRANSACTION |
control field |
20231207111801.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
fixed length control field |
231207b |||||||| |||| 00| 0 eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
International Standard Book Number |
9789188364807 |
041 ## - LANGUAGE CODE |
Language |
English |
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
Edition number |
23 |
Classification number |
006.31 |
Item number |
ADR |
100 ## - MAIN ENTRY--PERSONAL NAME |
Personal name |
Adrean, Kurt. |
240 ## - UNIFORM TITLE |
Uniform title |
<a href="Introduction to Deep Learning ">Introduction to Deep Learning </a> |
245 ## - TITLE STATEMENT |
Title |
Introduction to Deep Learning / |
Statement of responsibility, etc |
Kurt Adrean. |
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
Place of publication, distribution, etc |
Gotland : |
Name of publisher, distributor, etc |
Lynas Publishing, |
Date of publication, distribution, etc |
2023. |
300 ## - PHYSICAL DESCRIPTION |
Extent |
163 p. : |
Other physical details |
ill. ; |
Dimensions |
23 cm. |
505 ## - FORMATTED CONTENTS NOTE |
Title |
1. License<br/>2. Deep Learning Tutorials<br/>3. Getting started<br/>4. Classifying MNIST digits using Logistics Regression<br/>5. Multiplayer Perceptron<br/>6. Convolution Neural Networks (LeNet)<br/>7. Denoising Autoencoders (dA)<br/>8. Stacked Denoising Autoencoders (dA)<br/>9. Restricted Boltzman Machine (RBM)<br/>10. Deep Belief Networks<br/>11. Hybrid Monte-Carlo Sampling<br/>12. Recurrent Neural Networks with Word Embeddings<br/>13. LSTM Networks for Sentiment Analysis<br/>14. Modelling and Generating sequences of polyphonic music with RNN-RBM<br/>15. Miscellaneous<br/>Index |
520 ## - SUMMARY, ETC. |
Summary, etc |
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. |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
LSTM |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Boltzman Machine |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Logistics Regression |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Perceptron |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Convolution Neural Networks (LeNet) |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
MNIST |
942 ## - ADDED ENTRY ELEMENTS (KOHA) |
Source of classification or shelving scheme |
Dewey Decimal Classification |
Koha item type |
Text Books |