Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras
Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh
- 出版商: Packt Publishing
- 出版日期: 2018-08-31
- 售價: $1,800
- 貴賓價: 9.5 折 $1,710
- 語言: 英文
- 頁數: 438
- 裝訂: Paperback
- ISBN: 1788831306
- ISBN-13: 9781788831307
-
相關分類:
DeepLearning、Python、程式語言、TensorFlow
-
相關翻譯:
Python 遷移學習 (簡中版)
立即出貨 (庫存=1)
買這商品的人也買了...
-
$650$514 -
$719$683 -
$450$356 -
$980$774 -
$4,250$4,165 -
$680$578 -
$2,538Deep Learning for Medical Image Analysis
-
$530$419 -
$806CISSP 認證考試指南, 7/e (CISSP All-in-One Exam Guide, 7/e)
-
$1,680Learning Kali Linux: Security Testing, Penetration Testing, and Ethical Hacking
-
$2,300$2,254 -
$399MATLAB Machine Learning Recipes: A Problem-Solution Approach, 2/e (Paperback)
-
$480$379 -
$708$673 -
$1,020$969 -
$1,000$790 -
$1,395$1,325 -
$1,200$948 -
$580$458 -
$301特徵工程入門與實踐 (Feature Engineering Made Easy)
-
$520$406 -
$500$395 -
$520$411 -
$620$490 -
$2,880$2,736
相關主題
商品描述
Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem
Key Features
- Build deep learning models with transfer learning principles in Python
- implement transfer learning to solve real-world research problems
- Perform complex operations such as image captioning neural style transfer
Book Description
Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems.
The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples.
The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).
By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.
What you will learn
- Set up your own DL environment with graphics processing unit (GPU) and Cloud support
- Delve into transfer learning principles with ML and DL models
- Explore various DL architectures, including CNN, LSTM, and capsule networks
- Learn about data and network representation and loss functions
- Get to grips with models and strategies in transfer learning
- Walk through potential challenges in building complex transfer learning models from scratch
- Explore real-world research problems related to computer vision and audio analysis
- Understand how transfer learning can be leveraged in NLP
Who this book is for
Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.
Table of Contents
- Machine Learning Fundamentals
- Deep Learning Essentials
- Understanding Deep Learning Architectures
- Transfer Learning Fundamentals
- Unleash the Power of Transfer Learning
- Image Recognition and Classification
- Text Document Categorization
- Audio Identification and Categorization
- Deep Dream
- Neural Style Transfer
- Automated Image Caption Generator
- Image Colorization
商品描述(中文翻譯)
深度學習簡化:利用Python生態系統將監督式、非監督式和強化學習提升到新的水平
主要特點
- 使用Python建立具有轉移學習原則的深度學習模型
- 實施轉移學習以解決真實世界的研究問題
- 執行複雜操作,如圖像標題生成和神經風格轉移
書籍描述
轉移學習是一種機器學習(ML)技術,可以將在解決一組問題的過程中獲得的知識應用於解決其他相似的問題。
本書的目的有兩個:首先,我們專注於深度學習(DL)和轉移學習的詳細介紹,通過易於理解的概念和示例進行比較和對比。其次,我們重點關注使用TensorFlow、Keras和Python生態系統的實際示例和研究問題,並提供實踐示例。
本書首先介紹ML和DL的關鍵基本概念,然後描述和涵蓋重要的DL架構,如卷積神經網絡(CNN)、深度神經網絡(DNN)、循環神經網絡(RNN)、長短期記憶(LSTM)和膠囊網絡。然後,我們將轉移到轉移學習概念,例如模型凍結、微調、預訓練模型(包括VGG、Inception、ResNet)以及這些系統如何比DL模型更好地執行實際示例。在結尾章節中,我們將關注多個與計算機視覺、音頻分析和自然語言處理(NLP)等領域相關的真實案例和問題。
通過閱讀本書,您將能夠在自己的系統中實施DL和轉移學習原則。
你將學到什麼
- 建立具有圖形處理單元(GPU)和雲支持的DL環境
- 深入研究ML和DL模型的轉移學習原則
- 探索各種DL架構,包括CNN、LSTM和膠囊網絡
- 了解數據和網絡表示以及損失函數
- 掌握轉移學習中的模型和策略
- 解決從頭開始構建複雜轉移學習模型時可能遇到的挑戰
- 探索與計算機視覺和音頻分析相關的真實研究問題
- 了解如何在NLP中利用轉移學習
本書適合對象
《Python實戰轉移學習》適用於數據科學家、機器學習工程師、分析師和開發人員,他們對數據和應用最新轉移學習方法解決困難的真實世界問題感興趣。需要具備機器學習和Python的基本能力。
目錄
- 機器學習基礎
- 深度學習基本概念
- 理解深度學習架構
- 轉移學習基礎
- 發揮轉移學習的威力
- 圖像識別和分類
- 文本文檔分類
- 音頻識別和分類
- 深度夢境
- 神經風格轉移
- 自動圖像標題生成
- 圖像上色