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Senin, 03 Desember 2012

[F119.Ebook] Download I'm Still Smiling, My Breast Cancer Journal, by Barbara Black Fox

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Nonfiction portions taken from my journal, during the time I was diagnosed, underwent surgery, and then chemotherapy for breast cancer. Hopefully, it will help others who are going through--or know others who are dealing with similar trials.

Sales Rank: #2065577 in eBooks
Published on: 2013-03-19
Released on: 2013-03-19
Format: Kindle eBook

Most helpful customer reviews

1 of 1 people found the following review helpful.
Beating Cancer with a Smile
By Leita Berry
I'm amazed at the honesty and wit Barbara uncovered in this journal of her cancer experience. It was too short! I wanted to know more. Fortunately, Barbara is articulate and wry, honest and fair in her assessments. I felt like I wanted to hold her hand, or to have her hold mine when I go through trials. Brave sharing! Thanks.

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Minggu, 02 Desember 2012

[I449.Ebook] Ebook Download Machine Learning for the Web, by Andrea Isoni

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Key Features

Targets two big and prominent markets where sophisticated web apps are of need and importance.
Practical examples of building machine learning web application, which are easy to follow and replicate.
A comprehensive tutorial on Python libraries and frameworks to get you up and started.

Book Description

Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique book that helps bridge the gap between machine learning and web development. It focuses on the difficulties of implementing predictive analytics in web applications. We focus on the Python language, frameworks, tools, and libraries, showing you how to build a machine learning system. You will explore the core machine learning concepts and then develop and deploy the data into a web application using the Django framework. You will also learn to carry out web, document, and server mining tasks, and build recommendation engines. Later, you will explore Python’s impressive Django framework and will find out how to build a modern simple web app with machine learning features.

What you will learn

Get familiar with the fundamental concepts and some of the jargons used in the machine learning community
Use tools and techniques to mine data from websites
Grasp the core concepts of Django framework
Get to know the most useful clustering and classification techniques and implement them in Python
Acquire all the necessary knowledge to build a web application with Django
Successfully build and deploy a movie recommendation system application using the Django framework in Python

About the Author

Andrea Isoni is a data scientist, PhD, and physicist professional with extensive experience in software developer positions. He has an extensive knowledge of machine learning algorithms and techniques. He also has experience with multiple languages, such as Python, C/C++, Java, JavaScript, C#, SQL, HTML, and Hadoop.

Table of Contents

Introduction to Practical Machine Learning Using Python

Unsupervised Machine Learning

Supervised Machine Learning

Web Mining Techniques

Recommendation Systems

Getting Started with Django

Movie Recommendation System Web Application

Sentiment Analyser Application for Movie Reviews

Sales Rank: #224810 in Books
Published on: 2016-07-29
Released on: 2016-07-29
Original language: English
Dimensions: 9.25" h x .68" w x 7.50" l, 1.14 pounds
Binding: Paperback
298 pages

About the Author

Andrea Isoni

Most helpful customer reviews

4 of 5 people found the following review helpful.
Machine learning for novice data scientists and technicians, with lots of excellent coding examples in Pyhton
By Stefano Sabatini
Machine Learning for the Web by Andrea Isoni can be considered as an introduction to machine learning tools in Python for technicians already acquainted with machine learning theory.

The first chapters give a short overview of the most important machine learning algorithms used in machine learning scientific and industrial environments, and gives a short introduction to the adopted Python libraries to process data employing those algorithms.

The second part is more focused on technological aspects, and shows how to use Python to implement a web application complete with a recommendation and sentiment analysis system using the django framework and various Python libraries.

A somehow more detailed summary follows.

Chapter 1, Introduction to Practical Machine Learning Using Python

The more important Python libraries employed in the book are introduced here. This chapter can be considered as a tutorial on the various libraries, namely: NumPy, pandas, matplotlib. For the first two a complete tutorial is given, for the last one (matplotlib) only a short introduction. Other libraries are used in the book (namely: SciPy, scikit-learn (sklearn), scrapy), and are introduced later in the book.

Note that this chapter assumes the reader to have a basic understanding of the Python language (although not advanced programming topics are required to fully enjoy the book).

Chapter 2, Machine Learning Techniques: Unsupervised Learning

This chapter recalls the most important unsupervised learning algorithms, in particular clustering and data reduction algorithms. Every description
is complemented by a coding example in Python using numpy and the other libraries.

Note that although all the algorithms are fully described, the chapter assumes a detailed knowledge of the mathematical notation and understanding of the algorithms. So this is more to be intended as a reminder for the student rather than a full mathematical introduction to the main concepts.

Chapter 3, Supervised Machine Learning

This chapter provides an overview of the most employed supervised machine learning models: it starts with an explanation of the model error estimation, and proceeds with a description of several methods: based on linear regressions (ridge, lasso, logistic), k-nearest neighbours (KNN), Naive Bayes and Multinomial Naive Bayes, Gaussian Naive Bayes, decision trees, SVM.

The models are then compared using a testing dataset with numpy/pandas/sklearn and measuring several performance indices. This is done for classifiers and regression models using two different public datasets.

The chapter ends with a description of the Hidden Markov Chain model, together with a Python implementation.

Chapter 4, Web Mining Techniques

This chapter is about retrieving pages from the web, storing and processing them to extract relevant information. The chapter can be divided in the following sections.

Web structure mining: explores web crawling, indexing, and page ranking (in particular, the famous Page algorithm is explained).

Web content mining: parsing is discussed using the scrapy tool, natural language processing through the nltk library. Several information retrieval models are then considered and explained from a mathematical point of view. For each considered model (TF-IDF, Latent Semantic Analysis, Doc2Vec, Word2vec), a Python example is later presented in the context of creating a movie review query system, and the models performances are compared.

Postprocessing information: postprocessing is analyzed in the context of providing two distinct goals, select topics and provide subjective information related to user estimation (opinion/sentiment analysis). Latent Dirichlet Analysis is covered to provide means of classifying a text extracting latent topics. Sentiment analysis is then performed on a corpus of movie reviews, essentially applying several classifiers (Naive
Bayes, SVM and logistic regression) and testing their performances with nltk.

Chapter 5, Recommendation Systems

Given a set of users, and a set of items, and some user evaluations on the items, a recommendation systems tries to predict the non-evaluated items which may be appreciated by the user. This chapter explores the most important techniques adopted in the industry, and as usual provides an exampling implementation in Python using scipy with data from the MovieLens database.

In particular the chapter explores Content-based Filtering (CBF) and Collaborative Filtering (CF) methods. As usual, several complete examples are provided. Also, the chapter mentions hybrid approaches to recommendations, and recommendation system performance evaluation using supervised machine learning techniques.

Chapter 6, Getting Started with Django

This chapter provides a short tutorial on setting up a django server and writing a complete application for it. The chapter explains the code for a simple application storing addresses.The chapter covers the most important element of a django application, thus at the end of the reading you should be able to get a firm grasp of how it works.

Chapter 7, Movie Recommendation System Web Application

This chapter applies the knowledge developed in the previous chapters to implement a movie recommendation system using django and the
recommendation techniques reviewed in the previous chapters. In particular, the implemented system supports an information retrieval system (Term Frequency, Inverse Document Frequency (TF-IDF) model) to allow the user to find movies typing some relevant words, together with a recommendation system (based on CF item-based and log-likelihood ratio).

Chapter 8, Sentiment Analyser Application for Movie Reviews

This chapter is about the creation of a web application for movie reviews sentiment analysis. It employs data mining techniques to extract information related to movies from the web and perform sentiment analysis. The web crawling part is performed through the use of the scrapy library using the bing REST API. Natural language processing is performed through nltk: tokenization, selection of the most important words and bigrams, and classification using a Naive Bayes classifier. The fetched reviews are ranked according to the usual PageRank algorithm.

The book provides an extensive analytical index, but is missing a bibliography (although papers and online documentation are frequently referenced in the text).

...

Conclusions: this book gives a complete overview of the more important algorithms in the field of machine learning, and shows how to employ them in a typical production scenario. Given the complexity of the topics, it's probably more useful for the technician already familiar with the machine learning theory. Indeed, although the theory is shortly exposed in the first chapters, it assumes already some familiarity on the subject, so this cannot be considered an introduction to machine learning concepts and mathematical/statistical models.

If you are already somehow acquainted with machine learning theory and statistical methods, and want to know how to apply them to build a complete web application using the state-of-the-art tools and Python libraries, this book is a perfect and precious choice. Particularly appreciated is the care committed on providing extensive examples, which cover most if not all the algorithms reviewed in the book, and can be adopted as the foundation to implement similar machine learning systems based on Python.

Even in the case you are not a technician or a student of the subject, but you are simply curious about machine learning and/or in case you want to widen the range of your skill-set and knowledge to venture in a new expanding scientific/technological field, given its raising importance in technology and society, this book is a recommended reading, together with some more focused formation on its mathematical/statistical background.

0 of 0 people found the following review helpful.
Good introduction to machine learning in python
By Roberto Congiu
This book is a good introduction to machine learning that mixes a theoretical overview of the most used machine learning algorithms with a step by step guide on how to integrate machine learning in simple web applications, using the Django framework.

All the code and examples are in python, so I'd recommend this book if python is your language of choice.
I'd recommend this book if either you are a data scientist who's used to other tools (like R) and wants to learn how to build ML web apps in python, or if you're a python web developer who wants to learn machine learning. In this case, I'd recommend you also get a more theoretical book like Pattern Recognition and Machine Learning (Information Science and Statistics) .

The book goes over the different families of ML algorithms (supervised, unsupervised) showing the math behind it, and has code examples on how to use it in python. One slightly annoying thing on the PDF version is that the python code is printed as an image, so you can't copy and paste snippets from it.

Then it gives you a brief introduction to Django and how to build simple web applications with it. Finally, it merges the data science part with the web developing part, showing how to build a movie recommendation app with sentiment analysis. The Django part is just enough to get you started.

Things that the book does NOT cover is how to run these algorithms on massive datasets (aka big data), for which you'd need something like spark.

1 of 1 people found the following review helpful.
I highly recommend this book!
By Giovanni Pisani
This book is an excellent choice to fill the gap between Machine Learning and web development.
The use of Python, an easy to learn programming language, helps a lot in the comprehension and development of the algorithms. Chapter 1 is a very nice and concise beginning tutorial of the NumPy and Pandas Python modules.
Chapters 2 and 3 are also very useful for Machine Learning beginners like me. They encompass the most commonly used algorithms for unsupervised and supervised Machine Learning. Within the following chapters, the reader is pleasantly led across the main web data mining techniques, the most popular recommended systems used in a commercial environment, and finally into the Django framework to begin developing web applications.

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[C531.Ebook] Ebook Linux: The Quick and Easy Beginners Guide to Learning the Linux Command Line (Linux in 3 Days Book 1), by Jason Cannon

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If you want to learn how to use Linux and level up your career but are pressed for time, read on.
As the founder of the Linux Training Academy and an instructor of several courses, I've had the good fortune of helping thousands of people hone their Linux skills. Interacting with so many people who are just getting started with the Linux operating system has given me invaluable insight into the particular struggles and challenges people face at this stage.

One of the biggest challenges for people interested in learning the ins and outs of Linux is simply a lack of time. When you are working with a limited and extremely valuable resource you want to make sure you make the most of it.

The next biggest challenge for Linux newcomers is knowing where to start. There is so much information available that deciding what to focus your attention on first is a big enough hurdle to keep many people from even starting. What's worse is starting down the path of learning only to discover too many concepts, commands, and nuances that aren't explained. This kind of experience is frustrating and leaves you with more questions than answers.

That's why I've written this book.
Not only have I condensed the most important material into five sections, each designed to be consumed in a day, I've also structured the content in a logical and systematic manner. This way you'll be sure to make the most out of your time by learning the foundational aspects of Linux first and then building upon that foundation each day.

In Linux in 3 Days you will learn the most important concepts and commands, and be guided step-by-step through several practical and real-world examples. As new concepts, commands, or jargon are encountered they are explained in plain language, making it easy to understand.

Here is what you will learn by reading Linux in 3 Days:

How to get access to a Linux server if you don't already.

What a Linux distribution is and which one to choose.

What software is needed to connect to Linux from Mac and Windows computers. Screenshots included.

What SSH is and how to use it, including creating and using SSH keys.

The file system layout of Linux systems and where to find programs, configurations, and documentation.

The basic Linux commands you'll use most often.

Creating, renaming, moving, and deleting directories.

Listing, reading, creating, editing, copying, and deleting files.

Exactly how permissions work and how to decipher the most cryptic Linux permissions with ease.

How to use the nano, vi, and emacs editors.

Two methods to search for files and directories.

How to compare the contents of files.

What pipes are, why they are useful, and how to use them.

How to compress files to save space and make transferring data easy.

How and why to redirect input and output from applications.

How to customize your shell prompt.

How to be efficient at the command line by using aliases, tab completion, and your shell history.

How to schedule and automate jobs using cron.

How to switch users and run processes as others.

Where to go for even more in-depth coverage on each topic.

What you learn in Learn Linux in 5 Days applies to any Linux environment including Ubuntu, Debian, Linux Mint, RedHat, Fedora, OpenSUSE, Slackware, and more.

Scroll up, click the Buy Now With 1 Click button and get started learning Linux today!
This is the first book in a three book series.

Sales Rank: #310221 in eBooks
Published on: 2015-04-01
Released on: 2015-04-01
Format: Kindle eBook

Most helpful customer reviews

1 of 1 people found the following review helpful.
Not bad beginners read
By Dan
easy to follow had the basics and was prime what more can I want in a free book ? it was good

1 of 1 people found the following review helpful.
Good read.
By Doug Sanders
Very helpful. Good read.

0 of 1 people found the following review helpful.
Two Stars
By xd
all images within this ebook are not available.

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Sabtu, 01 Desember 2012

[C345.Ebook] Free PDF The National Geographic Magazine May, 1955, by Gilbert (ed.); Schellbach, Louis; Locke, Justin; Marden, Luis; Peterson, Willis; Klingel, G

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Contains full color photos. Spine is chipped and slightly torn. Size: 7" x 10". No marks. Binding is tight but edges are slightly rubbed. Edges very slightly spotted or marked. NOT ex-library. Shipped Weight: Under 1 kilogram. Category: Travel & Places; Inventory No: 023388.

Sales Rank: #5010930 in Books
Published on: 1955-05
Binding: Single Issue Magazine

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0 of 0 people found the following review helpful.
Great gift!
By Amazon Customer Ray F.
Exactly as expected. Very good.

0 of 0 people found the following review helpful.
Gilbert Klingel is the MAN!
By Hosel Jurme
Gilbert Klingel is the MAN!

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Kamis, 29 November 2012

[W606.Ebook] PDF Ebook The Grafter's Handbook, by R. J. Garner

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This practial manual on plant propagation by grafting is written in a lucid and concise style, and illustrated throughout with line drawings and half-tones. Since 1947, when it was first published, it has been a useful and informative reference for horticulturalists and amateurs. This is the first time that this work has been published in paperback.

Sales Rank: #862120 in Books
Published on: 1988
Original language: English
Number of items: 1
Dimensions: 8.00" h x 5.50" w x .75" l,
Binding: Paperback
323 pages

Review
'Mr Garner almost certainly knows more about vegetative propagation of tree and bush fruit than any man on earth; and what is more to the purpose, he knows how to convey his knowledge clearly in word and line.' Guardian 'This is not only a masterly textbook illustrated with very clear line drawings and photographs, but also a fascinating anthology of all the ways in which man has found it possible, for one reason or another, to put two varieties of plant together to make one.' Country Life

About the Author
Steven Bradley has written more than 30 gardening books, including Propagation Basics, winner of the Garden Writers' Guild Award for Best Practical Book. He is author of the booklets for the Ground Force series of gardening programmes and the Ground Force Weekend Workbook co-authored with Alan Titchmarsh, BBC Books. He writes for many gardening magazines, including the RHS Journal, BBC Gardeners' World, Garden Life, The Garden and Garden Answers as well as for the Sunday Express. Creator of the Masterclass series in Gardens Monthly, he is also a regular on BBC radio and lectures to students at Capel Manor College (Enfield), the Inchbold School of Design in London, leading specialist days at the English Gardening School in London.Robert John Garner was born in Fulbourn, Cambridge, in 1907. A scientist at the former East Malling Research Station from 1926 until 1972, he became an international authority on the vegetative propagation of woody plants. He was awarded an honourary MSc from the University of Reading in 1951 and the Veitch Memorial Medal by the Royal Horticultural Society for his outstanding contribution to the advancement of the art, science and practice of horticulture in 1980. Former President of the Horticultural Education Association and the Great Britain & Ireland region of the International Plant Propagators Society, and author of The Grafter's Handbook (1947) and The Propagation of Tropical Fruit Trees (1976), he also wrote numerous research papers for technical journals including The Journal of Horticultural Science and CAB's Technical Communications series published by the Imperial Bureau of Horticulture and Plantation Crops. After his death in 1993, East Malling Research Station opened the Robert Garner Laboratory in remembrance of his life's work.

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0 of 0 people found the following review helpful.
The bible for grafters
By Sundance
One of the bibles for grafters. Mine is well worn

2 of 2 people found the following review helpful.
Great book for gardeners!
By Laura Jian
Hobby gardeners: if you intend to do a lot of tree-plants-shrubs purchases for your garden, then consider buying this book; it's great for what the title says!

0 of 4 people found the following review helpful.
Fast shipping
By Richard Arnold
Fast shipping! The book is easy to read and seems to cover everything needed for me to graft.

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Senin, 19 November 2012

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Published on: 1600
Binding: Hardcover

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Rabu, 07 November 2012

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Binding: Board book

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