Mastering Python Design Patterns

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本書主要用Python語言構建CNN,主要關注如何以*佳的方式利用各種Python和深度學習庫(如Keras，TensorFlow和Caffe)來構建真實世界的應用程序。從構建CNN塊開始，基於*佳實踐來指導你實現真實的CNN模型及解決方案。你將學習創建圖像和視頻的創新解決方案，以解決複雜的機器學習和計算機視覺問題。

Python、TensorFlow、神經網絡和深度學習因人工智能的流行而成為當下IT領域的熱門關鍵詞。本書首先介紹了Python及其常用庫Numpy、Matplotlib和Scipy的基本使用方法；其次介紹了TensorFlow的基本知識及使用方法；然後介紹了神經網絡的基礎知識以及神經網絡基本應用――感知機、線性回歸與邏輯回歸的理論與實現；最後介紹了兩種熱門的深度神經網絡――卷積神經網絡和循環神經

設計模式（Design Patterns）是一套被反復使用、多數人知曉、無數工程師實踐的代碼設計經驗的總結，它是面向對象思想的高度提煉和模板化。本書帶你一起從生活的角度思考設計模式，以輕鬆有趣的小故事開始，由淺入深地講解每一種模式，思考每一種模式，總結每一種模式！力求用更通俗的語言闡述難懂的概念，用更簡單的語法實現複雜的邏輯，用更短小的代碼寫出強悍的程序！使枯燥乏味的概念變得更有樂趣和意義，希望能

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Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for Python, Fourth Edition, is focused on the development and implementation of statistically motivated, data-dri

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Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using r

本書重點介紹Python語言在數據處理與數據挖掘方面的應用技巧，主要包括數據分析基礎知識(數據收集與分析軟件、數據挖掘的分析基礎、簡單數據的統計分析)，數據分析高級方法(多元數據的綜合分析、時序數據的模型分析)，大數據基本處理方法(大數據分析基礎應用、文獻計量與科研評價、社會網絡分析方法、數據分析編程平臺)等內容。附錄中還提供了Python數據分析相關方法和函數等，方便讀者隨時查看。本書內容豐富，

本書共五個部分。第一部分（前10章）為基礎篇，介紹Python概述、語言基礎、分支和循環控制、序列（列表、元組、集合、字典）、數組和矩陣、字符串和正則表達式、函數和模塊、面向對象編程、文件操作和異常處理等內容。第二部分（後10章）為應用篇，介紹二維圖表、三維圖像、聲頻、視頻；Python操作的數據庫介紹MySQL、PostgreSQL、SQL Server、Oracle、SQLite、MongoD

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The text covers important algorithm design techniques, such as greedy algorithms, dynamic programming, and divide-and-conquer, and gives applications to contemporary problems. Techniques including Fast Fourier transform, KMP algorithm for string matching, CYK algorithm for context free parsing and gradient descent for convex function minimization are discussed in detail. The book's emphasis is on computational models and their effect on algorithm design. It gives insights into algorithm design techniques in parallel, streaming and memory hierarchy computational models. The book also emphasizes the role of randomization in algorithm design, and gives numerous applications ranging from data-structures such as skip-lists to dimensionality reduction methods.

The text covers important algorithm design techniques, such as greedy algorithms, dynamic programming, and divide-and-conquer, and gives applications to contemporary problems. Techniques including Fast Fourier transform, KMP algorithm for string matching, CYK algorithm for context free parsing and gradient descent for convex function minimization are discussed in detail. The book's emphasis is on computational models and their effect on algorithm design. It gives insights into algorithm design techniques in parallel, streaming and memory hierarchy computational models. The book also emphasizes the role of randomization in algorithm design, and gives numerous applications ranging from data-structures such as skip-lists to dimensionality reduction methods.

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