- Main
- Computers - Artificial Intelligence (AI)
- Practical Machine Learning for Computer...
Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images
Valliappa Lakshmanan, Martin Görner, Ryan Gillardدا کتاب تاسو ته څنګه خواښه شوه؟
د بار شوي فایل کیفیت څه دئ؟
تر څو چې د کتاب کیفیت آزمایښو وکړئ، بار ئې کړئ
د بار شوو فایلونو کیفیتی څه دئ؟
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 preprocessing, model design, model training, evaluation, deployment, and interpretability.
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.
You'll learn how to:
• Design ML architecture for computer vision tasks
• Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task
• Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model
• Preprocess images for data augmentation and to support learnability
• Incorporate explainability and responsible AI best practices
• Deploy image models as web services or on edge devices
• Monitor and manage ML models
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.
You'll learn how to:
• Design ML architecture for computer vision tasks
• Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task
• Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model
• Preprocess images for data augmentation and to support learnability
• Incorporate explainability and responsible AI best practices
• Deploy image models as web services or on edge devices
• Monitor and manage ML models
درجه (قاطیغوری(:
کال:
2021
خپرونه:
1
خپرندویه اداره:
O'Reilly Media
ژبه:
english
صفحه:
481
ISBN 10:
1098102363
ISBN 13:
9781098102364
ISBN:
B09B164FBM
فایل:
PDF, 56.15 MB
ستاسی تیګی:
IPFS:
CID , CID Blake2b
english, 2021
په آن لاین ډول لوستل
- کاپی کول
- pdf 56.15 MB Current page
- Checking other formats...
- ته بدلول
- Unlock conversion of files larger than 8 MBPremium
غواړئ کتاب پلورنځي ته اضافه وکړئ؟ مونږ سره د support@z-lib.do له لارې اړیکه ونیسئ
د ۱- ۵ دقیقو په جریان کې فایل ستاسی ایمل ته دررسیږی.
د ۱-۵ دقیقو په ترڅ کښې به فایل ستاسو د ټیلیګرام آکاونټ ته وسپارل شي.
یادونه: مطمئن شئ چې تاسو خپل آګاونټ د Z-Library Telegram بوټ سره تړلی دی.
د ۱-۵ دقیقو په ترڅ کښې به فایل ستاسو د Kindle وسیلې ته وسپارل شي.
ملاحظه هر کتاب چي تاسي Kindle ته ليږئ باید تصدیق شی. خپله الکترونیکی پوسته تفتیش کړئ چې پکښې باید د Amazon Kindle Support له خوا مکتوب وی.
ته بدلون په کار دي
ته بدلون ناکام شو
Premium benefits
- Send to eReaders
- Increased download limit
- File converter
- د لټون نورې نبیجې
- More benefits