Python Deep Learning Projects: Investigate and Implement Deep Learning Architectures for building intelligent systems
Matthew Lamons, Rahul Kumar, Abhishek Nagaraja
- 出版商: Packt Publishing
- 出版日期: 2018-10-31
- 售價: $1,940
- 貴賓價: 9.5 折 $1,843
- 語言: 英文
- 頁數: 472
- 裝訂: Paperback
- ISBN: 1788997093
- ISBN-13: 9781788997096
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相關分類:
Python、程式語言、DeepLearning
海外代購書籍(需單獨結帳)
相關主題
商品描述
Insightful practical projects to master deep learning and neural network architectures using Python, Keras and MXNet
Key Features
- Rich projects on computer vision, NLP, and image processing
- Build your own neural network and explore innumerable possibilities with deep learning
- Explore the power of Python for deep learning in various scenarios using insightful projects
Book Description
Deep Learning has quietly revolutionized every field of Artificial Intelligence, enabling the development of applications that a few years ago were considered almost impossible.
This book will provide all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each new project will build upon the experience and knowledge accumulated in the previous ones, allowing the reader to progressively master the subject.
You will learn neural network models implementing a text classifier system using Recurrent Neural network model (RNN) and optimize it to understand the shortcomings you might come across while implementing a simple deep learning system. If you are looking to implement intelligent systems like Automatic Machine Translation, Handwriting Generation, Character Text Generation, Object Classification in Photographs, Colorization of Images, Image Caption Generation, Character Text Generation or Automatic Game Playing into your application then this book is for you.
By the end of this book, you will come across various Recurrent and Convolutional Neural network implementations with practical hands-on to modeling concepts like regularization, Gradient clipping, and gradient normalization, LSTM, Bidirectional RNN's through a series engaging projects.
What you will learn
- Set up a Deep Learning development environment on AWS, using GPU-powered instances and the Deep Learning AMI
- Implement Sequence to Sequence Networks for modeling natural language processing
- Develop an end-to-end speech recognition system
- Build a system for pixel-wise semantic labeling of an image
- Develop a system that generates images and their regions
Who This Book Is For
This book is for developers, data scientists, or enthusiasts, who have sound knowledge of python programming, basics of machine learning, and want to break into deep learning, either for opening a new career opportunity or for realizing their own AI projects.
商品描述(中文翻譯)
深入學習與神經網絡架構的實用專案,使用 Python、Keras 和 MXNet 精通掌握
主要特點
- 豐富的計算機視覺、自然語言處理和圖像處理專案
- 建立自己的神經網絡,探索深度學習的無限可能性
- 通過深入的專案探索 Python 在各種情境下的深度學習能力
書籍描述
深度學習已悄然革新了人工智慧的每一個領域,使得幾年前幾乎被認為不可能的應用得以實現。
本書將提供實施計算語言學和計算機視覺領域複雜深度學習專案所需的所有知識。每個新專案都將建立在前一個專案所累積的經驗和知識之上,讓讀者逐步掌握該主題。
您將學習使用遞迴神經網絡模型(RNN)實現文本分類系統的神經網絡模型,並優化它以理解在實施簡單深度學習系統時可能遇到的缺陷。如果您希望在應用中實施智能系統,如自動機器翻譯、手寫生成、字符文本生成、照片中的物體分類、圖像著色、圖像標題生成、字符文本生成或自動遊戲播放,那麼這本書適合您。
在本書結束時,您將接觸到各種遞迴和卷積神經網絡的實現,並通過一系列引人入勝的專案進行實踐,學習建模概念,如正則化、梯度裁剪和梯度正規化、LSTM、雙向 RNN。
您將學到的內容
- 在 AWS 上設置深度學習開發環境,使用 GPU 驅動的實例和深度學習 AMI
- 實施序列到序列網絡以建模自然語言處理
- 開發端到端的語音識別系統
- 建立圖像的像素級語義標記系統
- 開發生成圖像及其區域的系統
本書適合誰
本書適合開發人員、數據科學家或熱衷者,他們對 Python 編程有良好的知識,了解機器學習的基本概念,並希望進入深度學習領域,無論是為了開啟新的職業機會,還是實現自己的 AI 專案。