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計(jì)算機(jī)視覺機(jī)器學(xué)習(xí)實(shí)踐（影印版）
計(jì)算機(jī)視覺機(jī)器學(xué)習(xí)實(shí)踐（影印版）
Valliappa Lakshmanan, Martin Gorner, Ryan Gillard 著
出版時(shí)間：2022年06月
頁數(shù)：463
“本書全面介紹了深度計(jì)算機(jī)視覺的最新技術(shù)，以及在 Keras中構(gòu)建端到端生產(chǎn)系統(tǒng)以解決實(shí)際問題的最佳實(shí)踐。”
一Francois Chollet深度學(xué)習(xí)研究員以及Keras創(chuàng)造者

本書向你展示了如何使用機(jī)器學(xué)習(xí)（ML）模型從圖像中提取信息。ML工程師和數(shù)據(jù)科學(xué)家將學(xué)習(xí)行之有效的ML技術(shù)來解決各種圖像問題，包括分類、目標(biāo)檢測、自動(dòng)編碼器、圖像生成、計(jì)數(shù)和添加字幕。本書很好地介紹了端到端的深度學(xué)習(xí):數(shù)據(jù)集創(chuàng)建、數(shù)據(jù)預(yù)處理、模型設(shè)計(jì)、模型訓(xùn)練、評估、部署和可解釋性。
Google工程師Valliappa Lakshmanan、Martin Gorner、Ryan Gillard為你展示了如何開發(fā)精準(zhǔn)且可解釋的計(jì)算機(jī)視覺ML模型，并以靈活且可維護(hù)的方式使用健壯的ML架構(gòu)將其投入大規(guī)模生產(chǎn)。你將學(xué)習(xí)如何使用TensorFlow和Keras編寫的模型進(jìn)行設(shè)計(jì)、訓(xùn)練、評估和預(yù)測。
你會(huì)學(xué)到:
● 為計(jì)算機(jī)視覺任務(wù)設(shè)計(jì)ML架構(gòu)
● 選擇適合任務(wù)的模型(比如ResNet、SqueezeNet或EfficientNet)
● 創(chuàng)建端到端的ML管道來訓(xùn)練、評估、部署、解釋你的模型
● 對圖像進(jìn)行預(yù)處理，以增強(qiáng)數(shù)據(jù)并支持可學(xué)習(xí)性
● 結(jié)合可解釋性和可靠的AI最佳實(shí)踐
● 將圖像模型部署為Web服務(wù)或邊緣設(shè)備
● 監(jiān)測和管理ML模型
● 使用協(xié)作過濾設(shè)計(jì)出一套仿Netflix的推薦系統(tǒng)
  1. Preface
  2. 1. Machine Learning for Computer Vision
  3. Machine Learning
  4. Deep Learning Use Cases
  5. Summary
  6. 2. ML Models for Vision
  7. A Dataset for Machine Perception
  8. A Linear Model Using Keras
  9. A Neural Network Using Keras
  10. Summary
  11. Glossary
  12. 3. Image Vision
  13. Pretrained Embeddings
  14. Convolutional Networks
  15. The Quest for Depth
  16. Modular Architectures
  17. Neural Architecture Search Designs
  18. Beyond Convolution: The Transformer Architecture
  19. Choosing a Model
  20. Summary
  21. 4. Object Detection and Image Segmentation
  22. Object Detection
  23. Segmentation
  24. Summary
  25. 5. Creating Vision Datasets
  26. Collecting Images
  27. Data Types
  28. Manual Labeling
  29. Labeling at Scale
  30. Automated Labeling
  31. Bias
  32. Creating a Dataset
  33. Summary
  34. 6. Preprocessing
  35. Reasons for Preprocessing
  36. Size and Resolution
  37. Training-Serving Skew
  38. Data Augmentation
  39. Forming Input Images
  40. Summary
  41. 7. Training Pipeline
  42. Efficient Ingestion
  43. Saving Model State
  44. Distribution Strategy
  45. Serverless ML
  46. Summary
  47. 8. Model Quality and Continuous Evaluation
  48. Monitoring
  49. Model Quality Metrics
  50. Quality Evaluation
  51. Summary
  52. 9. Model Predictions
  53. Making Predictions
  54. Online Prediction
  55. Batch and Stream Prediction
  56. Edge ML
  57. Summary
  58. 10. Trends in Production ML
  59. Machine Learning Pipelines
  60. Explainability
  61. No-Code Computer Vision
  62. Summary
  63. 11. Advanced Vision Problems
  64. Object Measurement
  65. Counting
  66. Pose Estimation
  67. Image Search
  68. Summary
  69. 12. Image and Text Generation
  70. Image Understanding
  71. Image Generation
  72. Image Captioning
  73. Summary
  74. Afterword
  75. Index
書名：計(jì)算機(jī)視覺機(jī)器學(xué)習(xí)實(shí)踐（影印版）
國內(nèi)出版社：東南大學(xué)出版社
出版時(shí)間：2022年06月
頁數(shù)：463
書號：978-7-5641-9976-0
原版書書名：Practical Machine Learning for Computer Vision
原版書出版商：O'Reilly Media
Valliappa Lakshmanan
 
Valliappa (Lak) Lakshmanan是Google Cloud的數(shù)據(jù)分析和AI解決方案負(fù)責(zé)人。他的團(tuán)隊(duì)借助BigQuery和Google Cloud上的其他數(shù)據(jù)分析、機(jī)器學(xué)習(xí)產(chǎn)品，構(gòu)建軟件解決方案來解決業(yè)務(wù)問題。

Valliappa Lakshmanan是知名高管，與管理層其他同仁和數(shù)據(jù)科學(xué)團(tuán)隊(duì)一起用數(shù)據(jù)和AI創(chuàng)造價(jià)值。
 
 
Martin Gorner
 
Martin Gorner是Keras/TensorFlow的產(chǎn)品經(jīng)理，專注于使用最先進(jìn)的模型改善開發(fā)人員的體驗(yàn)。
 
 
Ryan Gillard
 
Ryan Gillard是Google Cloud專業(yè)服務(wù)組織的AI工程師，負(fù)責(zé)為各行各業(yè)構(gòu)建ML模型。他的職業(yè)生涯是從醫(yī)院和保健行業(yè)的研究科學(xué)家開始的。
 
 
The bird on the cover of Practical Machine Learning for Computer Vision is an emerald toucanet (Aulacorhynchus prasinus), the smallest species of toucan. Central and South America have large populations from the cloud forests of Costa Rica to Venezuela.
Vibrant green feathers camouflage emerald toucanets in the tropics. Adults typically measure 12–13 inches long, weigh just over 5 ounces, and live 10–11 years in the wild. Their beaks are colorful: yellow on top, a white outline, and red or black on the bottom. They eat fruit and insects, as well as small lizards and the eggs and young of other birds. Groups of about eight will hunt and forage together. Emerald toucanets build their nests by enlarging the nests of smaller birds. The male and female trade off shifts in the nest, incubating, feeding, and cleaning their chicks.
Deforestation has driven emerald toucanets into shade coffee farms. Overall, their population is decreasing. Many of the animals on O’Reilly’s covers are endangered; all of them are important to the world.
購買選項(xiàng)
定價(jià)：148.00元
書號：978-7-5641-9976-0
出版社：東南大學(xué)出版社