Deep Learning with TensorFlow - Second Edition: Explore neural networks with Python

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide

Key Features

• Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow
• Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide
• Real-world contextualization through some deep learning problems concerning research and application

Book Description

Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data and has been fully updated to the latest version of TensorFlow 1.x.

Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you'll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context.

After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.

What you will learn

• Learn about machine learning landscapes along with the historical development and progress of deep learning
• Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x
• Access public datasets and utilize them using TensorFlow to load, process, and transform data
• Use TensorFlow on real-world datasets, including images, text, and more
• Learn how to evaluate the performance of your deep learning models
• Using deep learning for scalable object detection and mobile computing
• Train machines quickly to learn from data by exploring reinforcement learning techniques
• Explore active areas of deep learning research and applications

Who This Book Is For

The book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.

深入研究神經網絡，實現深度學習算法，並通過這本全面的TensorFlow指南探索數據抽象的層次。

主要特點:
- 學習如何使用Google的TensorFlow實現深度學習的高級技術
- 通過這本全面的指南，探索深度神經網絡和數據抽象的層次
- 通過研究和應用一些深度學習問題，實現現實世界的情境化

書籍描述:
深度學習是機器學習之後的一個步驟，具有更高級的實現。機器學習不再僅僅是學術界的專利，而是通過廣泛的應用成為主流實踐，而深度學習則處於領先地位。作為一名數據科學家，如果你想探索數據抽象層次，這本書將成為你的指南。本書展示了如何在現實世界中利用複雜的原始數據進行開發，並已完全更新到最新版本的TensorFlow 1.x。

在整本書中，你將學習如何為機器學習系統實現深度學習算法，並將它們整合到你的產品中，包括搜索、圖像識別和語言處理。此外，你還將學習如何分析和改進深度學習模型的性能。通過將算法與基準進行比較，並利用機器智能從信息中學習並確定特定情境下的理想行為，可以實現這一點。

完成本書後，你將熟悉機器學習技術，特別是使用TensorFlow進行深度學習，並準備將你的知識應用於研究或商業項目。

你將學到什麼:
- 了解機器學習的發展歷程以及深度學習的歷史發展和進展
- 了解最新的TensorFlow 1.x中的深度機器智能和GPU計算
- 存取公共數據集並使用TensorFlow加載、處理和轉換數據
- 在真實世界的數據集上使用TensorFlow，包括圖像、文本等
- 學習如何評估深度學習模型的性能
- 使用深度學習進行可擴展的物體檢測和移動計算
- 通過探索強化學習技術，快速訓練機器從數據中學習
- 探索深度學習研究和應用的活躍領域

本書適合對機器學習和機器智能感興趣的廣大讀者。假設讀者具備一定程度的編程能力，並對計算機科學技術和技術有基本的了解，包括對計算機硬件和算法的基本認識。需要具備基礎的線性代數和微積分知識。