Buku Text Informatika

Practical mashine learning for computer vision end-to-end machine learning for image

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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

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Ketersediaan

13994

006.37 LAK p c1

UPT. PERPUSTAKAAN PUSAT (Rak Tandon)

Tersedia - No Loan

Informasi Detail

Judul Seri

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No. Panggil

006.37 LAK p

Penerbit

Newton, Massachusetts : O'Reilly., 2021

Deskripsi Fisik

xv, 464 hlm.; 23 cm

Bahasa

English

ISBN/ISSN

978-1-098-10236-4

Klasifikasi

006.37

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

Ed. I

Subjek

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Info Detail Spesifik

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Pernyataan Tanggungjawab

Valliappa Lakshmanan

Versi lain/terkait

Lampiran Berkas

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