Deep Learning Cookbook: Practical Recipes to Get Started Quickly
暫譯: 深度學習食譜:快速入門的實用範例
Douwe Osinga
- 出版商: O'Reilly
- 出版日期: 2018-07-17
- 定價: $2,100
- 售價: 8.0 折 $1,680
- 語言: 英文
- 頁數: 252
- 裝訂: Paperback
- ISBN: 149199584X
- ISBN-13: 9781491995846
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相關分類:
DeepLearning
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相關翻譯:
深度學習實戰 (簡中版)
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商品描述
Recent developments in deep learning have put the field center stage for innovation in software engineering. New algorithms and techniques in academia hold promise for many real world problems, and new machine learning platforms are powerful, but aren’t necessarily easy to get started with.
With this hands-on cookbook, you'll discover that deep learning doesn't need to be intimidating. Aimed at readers who are new to deep learning, this cookbook enables you to solve problems quickly, using the most appropriate platform for each application. You'll learn how to leverage the work of Google by reusing pre-trained networks, use non-final layers to map data, and build recommender systems out of any correlation data.
- Work with step-by-step recipes that address familiar problems in areas such as text embeddings, text labeling and generation, and image classification and generation
- Walk through a practical solution for each recipe, using modern machine learning frameworks
- Learn how your newly-trained models can be easily ported for use in production settings
- Build applications that go from interesting results to serving real users
- Use deep learning in production, including how to query embeddings with the Postgres database, and how export and serve models using TensorFlow
- Set up a microservice using Python, and run models on mobile devices
商品描述(中文翻譯)
最近在深度學習方面的發展使得這個領域成為軟體工程創新的中心。學術界的新算法和技術對許多現實世界的問題充滿希望,而新的機器學習平台雖然強大,但不一定容易上手。
這本實用的食譜書將讓你發現深度學習並不需要令人畏懼。這本食譜書針對對深度學習不熟悉的讀者,幫助你快速解決問題,並使用最適合每個應用的平臺。你將學會如何利用 Google 的工作,重用預訓練的網絡,使用非最終層來映射數據,並根據任何相關數據構建推薦系統。
- 使用逐步的食譜來解決文本嵌入、文本標記和生成、圖像分類和生成等熟悉問題
- 針對每個食譜,使用現代機器學習框架逐步走過實用解決方案
- 學習如何輕鬆地將新訓練的模型移植到生產環境中
- 構建從有趣結果到服務真實用戶的應用
- 在生產環境中使用深度學習,包括如何使用 Postgres 數據庫查詢嵌入,以及如何使用 TensorFlow 匯出和服務模型
- 使用 Python 設置微服務,並在移動設備上運行模型