MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02837nam a22002897a 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
21994071 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
IIITD |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20240504150404.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
210414s2021 mau 000 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
|---|
| LC control number |
2021937264 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9789356063976 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
DLC |
| Language of cataloging |
eng |
| Description conventions |
rda |
| Transcribing agency |
DLC |
| 042 ## - AUTHENTICATION CODE |
|---|
| Authentication code |
pcc |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Item number |
EKM-L |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ekman, Magnus |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Learning deep learning : |
| Remainder of title |
theory and practice of neural networks, computer vision, natural language processing, and transformers using tensorflow |
| Statement of responsibility, etc |
by Magnus Ekman. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
New Delhi : |
| Name of publisher, distributor, etc |
Pearson, |
| Date of publication, distribution, etc |
©2023 |
| 263 ## - PROJECTED PUBLICATION DATE |
|---|
| Projected publication date |
2108 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
lv, 554 p. : |
| Other physical details |
ill. ; |
| Dimensions |
23 cm. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
Chapter 1. The Rosenblatt Perceptron |
| -- |
Chapter 2. Gradient-Based Learning |
| -- |
Chapter 3. Sigmoid Neurons and Backpropagation |
| -- |
Chapter 4. Fully Connected Networks Applied to Multiclass Classification |
| -- |
Chapter 5. Toward DL: Frameworks and Network Tweaks |
| -- |
Chapter 6. Fully Connected Networks Applied to Regression |
| -- |
Chapter 7. Convolutional Neural Networks Applied to Image Classification |
| -- |
Chapter 8. Deeper CNNs and Pretrained Models |
| -- |
Chapter 9. Predicting Time Sequences with Recurrent Neural Networks |
| -- |
Chapter 10. Long Short-Term Memory |
| -- |
Chapter 11. Text Autocompletion with LSTM and Beam Search |
| -- |
Chapter 12. Neural Language Models and Word Embeddings |
| -- |
Chapter 13. Word Embeddings from word2vec and GloVe |
| -- |
Chapter 14. Sequence-to-Sequence Networks and Natural Language Translation |
| -- |
Chapter 15. Attention and the Transformer |
| -- |
Chapter 16. One-to-Many Network for Image Captioning |
| -- |
Chapter 17. Medley of Additional Topics |
| -- |
Chapter 18. Summary and Next Steps |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
"Deep learning is at the heart of many of today's most exciting advances in machine learning and artificial intelligence. Pioneering applications at companies like Tesla, Google, and Facebook are now being followed by massive investments in fields ranging from finance to healthcare. Now, there's a complete guide to deep learning with TensorFlow, the #1 Python library for building these breakthrough applications. Magnus Ekman illuminates both the underlying concepts and the hands-on programming techniques you'll need, even if you have no machine learning experience. Throughout, you'll find concise, well-annotated code examples using TensorFlow and the Keras API; for comparison and easy migration between frameworks, complementary examples in PyTorch are provided online. Ekman also explains enough of the mathematics to help newcomers grasp how deep learning actually works. The guide concludes by previewing emerging trends in deep learning, and exploring the challenging ethical issues surrounding its use"-- |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Deep Learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Neural Networks |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
|---|
| a |
0 |
| b |
ibc |
| c |
orignew |
| d |
2 |
| e |
epcn |
| f |
20 |
| g |
y-gencatlg |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |
