MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02000cam a2200229 i 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
220901s2021 cc a b 001 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9789391043834 |
| 042 ## - AUTHENTICATION CODE |
|---|
| Authentication code |
pcc |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
005.74015 |
| Cutter |
LAK |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Lakshmanan, Valliappa, |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Practical machine learning for computer vision : |
| Remainder of title |
end-to-end machine learning for images |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
First edition. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Mumbai |
| Name of publisher, distributor, etc |
Shroff Publishers & Distributors Pvt. Ltd. |
| Date of publication, distribution, etc |
2021 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvi, 463 pages : |
| 520 ## - Remark |
|---|
| Summary, etc |
This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data pre-processing, model design, model training, evaluation, deployment, and interpretability.<br/><br/>Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.<br/><br/>You'll learn how to:<br/><br/>Design ML architecture for computer vision tasks<br/>Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task<br/>Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model<br/>Pre-process images for data augmentation and to support learnability<br/>Incorporate explainability and responsible AI best practices<br/>Deploy image models as web services or on edge devices<br/>Monitor and manage ML models |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer vision. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Görner, Martin, |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Gillard, Ryan, |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
|---|
| a |
Business Analytics |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Item type |
Book |
