Deep Learning By Example

Ahmed Menshawy

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商品描述

Key Features

  • Get your first experience with deep learning with this easy-to-follow guide
  • Implement neural networks with the easiest, developer-friendly tools and techniques in the market.

Book Description

Deep Learning has made some huge and significant contributions and it's one of the mostly adopted techniques in order to drive insights from your data nowadays. Google developed one of the most used libraries (aka. TensorFlow) to use in order to build fast, robust against an error-prone and scale deep learning algorithms that can run on both CPU and GPU.

This book is a starting point for those who are keen on knowing about deep learning and implementing it, but do not have extensive background in machine learning. We will start with introducing you with Data science for performing data analysis, machine learning, and eventually deep learning. Then, you will explore algorithms and various techniques that lead into efficient data processing. You will learn to clean, mine, and analyze data. Once you are comfortable with some analysis, you will then move to creating machine learning models that will eventually lead you to neural networks. You will get familiar with basics of deep learning and explore various tools that enable deep learning in a powerful yet user friendly manner. While all of this is being taught, spread across the book, we will be using intuitive examples like Titanic survivor prediction, Housing price predictor, etc. teaching implementations of each of the concept. With a very low starting point, this book will enable a regular developer to get hands on experience with deep learning.

By the end of this book, you will learn all the essentials needed to explore and understand what is deep learning and will perform deep learning tasks first hand.

What you will learn

  • Learn about Data Science, its challenges and how to tackle them.
  • Learn the basics of Data Science and modern best practices with a Titanic Example.
  • Get familiarized with one of the most powerful platforms for Deep Learning(DL), TensorFlow 1.x.
  • Basic of Deep Learning and modern best practices with a digit classification problem of MNIST.
  • Dive into imaging problems by looking at early lung cancer detection and emotion recognition using CNN.
  • Apply deep learning to other domains like Language Modeling, ChatBots and Machine Translation using the one of the powerful architectures of DL, RNN.

商品描述(中文翻譯)

主要特點


  • 透過這本易於理解的指南,初次體驗深度學習

  • 使用市場上最簡單、開發者友善的工具和技術來實現神經網絡

書籍描述

深度學習在當今從數據中獲取洞察力的技術中做出了巨大而重要的貢獻,並且是最常被採用的技術之一。Google開發了一個最常用的庫(即TensorFlow),用於構建快速、強大且能夠在CPU和GPU上運行的抗錯誤和可擴展的深度學習算法。

本書是對於那些渴望了解深度學習並實施它,但在機器學習方面沒有廣泛背景的人的起點。我們將從介紹數據科學開始,以進行數據分析、機器學習,最終進入深度學習。然後,您將探索算法和各種技術,以實現高效的數據處理。您將學習清理、挖掘和分析數據。一旦您對某些分析感到舒適,您將開始創建機器學習模型,最終導致神經網絡。您將熟悉深度學習的基礎知識,並探索各種工具,以強大而又易於使用的方式實現深度學習。在整本書中,我們將使用直觀的例子,如鐵達尼號生還者預測、房價預測等,來教授每個概念的實現。這本書從非常基礎的起點開始,使一般開發者能夠親身體驗深度學習。

通過閱讀本書,您將學習所有必要的知識,探索和理解深度學習,並首次進行深度學習任務。

你將學到什麼


  • 了解數據科學,面臨的挑戰以及如何應對

  • 通過鐵達尼號的例子學習數據科學的基礎知識和現代最佳實踐

  • 熟悉最強大的深度學習平台之一,TensorFlow 1.x

  • 通過MNIST數字分類問題,學習深度學習的基礎知識和現代最佳實踐

  • 通過CNN,深入研究早期肺癌檢測和情緒識別等圖像問題

  • 使用RNN,將深度學習應用於其他領域,如語言建模、聊天機器人和機器翻譯