Home eBooks Download › learn library classification

Learn Library Classification

Download Learn Library Classification PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Learn Library Classification book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Learn Library Classification

DOWNLOAD
Author : Anil Kumar Dhiman
language : en
Publisher: Ess Ess Publication
Release Date : 2005

Learn Library Classification written by Anil Kumar Dhiman and has been published by Ess Ess Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Language Arts & Disciplines categories.

"Learning Library Science Series was planned to give the students of LIS a complete and comprehensive study material so as to familiarize them with all there is to learn about basics of library science. This series has been divided into six parts, each of which is dedicated to one basic aspect of library and information science. The present series consists of six books in all. Its first part deals with Library and Society, second is Learn Library Management, third is Learn Library Classification (Theory), Fourth being Learn Library Cataloguing (Theory), fifth, Learn Reference Services, Information Services and their Sources and the last and sixth being Learn Computer Basics and its Application to Libraries."

Learn Library Cataloguing

DOWNLOAD
Author : Anil Kumar Dhiman
language : en
Publisher: Ess Ess Publication
Release Date : 2005-08

Learn Library Cataloguing written by Anil Kumar Dhiman and has been published by Ess Ess Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-08 with categories.

Classification Made Simple

DOWNLOAD
Author : Eric J Hunter
language : en
Publisher: Routledge
Release Date : 2017-11-01

Classification Made Simple written by Eric J Hunter and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-01 with Social Science categories.

This title was first published in 2002: This is an attempt to simplify the initial study of classification as used for information retrieval. The text adopts a gradual progression from very basic principles, one which should enable the reader to gain a firm grasp of one idea before proceeding to the next.

Learn Information And Reference Sources And Services

DOWNLOAD
Author : Anil Kumar Dhiman
language : en
Publisher: Ess Ess Publication
Release Date : 2005-08

Learn Information And Reference Sources And Services written by Anil Kumar Dhiman and has been published by Ess Ess Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-08 with Information resources categories.

Learn Library Management

DOWNLOAD
Author : Anil Kumar Dhiman
language : en
Publisher: Ess Ess Publication
Release Date : 2005-08

Learn Library Management written by Anil Kumar Dhiman and has been published by Ess Ess Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-08 with categories.

Decoding Library Classification A Guide To Mastering Dewey Decimal Classification

DOWNLOAD
Author : Pasquale De Marco
language : en
Publisher: Pasquale De Marco
Release Date : 2025-07-09

Decoding Library Classification A Guide To Mastering Dewey Decimal Classification written by Pasquale De Marco and has been published by Pasquale De Marco this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-09 with Reference categories.

Dewey Decimal Classification: A Comprehensive Guide to Mastering Library Organization is an indispensable resource for library professionals, students, and researchers seeking to navigate the complexities of library classification. This comprehensive guide delves into the history, structure, and application of Dewey Decimal Classification (DDC), providing a thorough understanding of this widely used system for organizing library materials. Throughout this comprehensive guide, readers will embark on a journey to unravel the inner workings of DDC, exploring its fundamental principles, practical applications, and the technological advancements that have shaped its evolution. They will delve into the history of DDC, tracing its origins from Melvil Dewey's groundbreaking vision to its widespread adoption as a global standard for library classification. Furthermore, readers will dissect the structure of DDC, examining its hierarchical organization and the various components that contribute to its effectiveness. The book delves into the intricacies of main classes, subclasses, and notations, providing a clear understanding of how these elements work together to create a cohesive and comprehensive classification system. The practical application of DDC is a central focus of this guide. Readers will explore the processes involved in assigning classification numbers to library materials, ensuring accurate and consistent organization. Additionally, they will delve into the use of DDC in various library settings, including public libraries, academic libraries, and special libraries, highlighting its versatility and adaptability across different environments. As technology continues to reshape the library landscape, DDC remains a dynamic and evolving system, embracing the digital age with open arms. Readers will investigate the impact of technology on DDC, examining how online classification systems, digital libraries, and metadata standards have transformed the way libraries organize and deliver information. Delving into the international landscape of library classification, this book explores the relationship between DDC and other prominent classification systems, such as the Universal Decimal Classification (UDC), the Library of Congress Classification (LCC), and the British National Bibliography (BNB) Classification. Readers will delve into the similarities and differences between these systems, highlighting their respective strengths and applications. If you like this book, write a review!

Learn Library And Society

DOWNLOAD
Author : Anil Kumar Dhiman
language : en
Publisher: Ess Ess Publication
Release Date : 2005-08-01

Learn Library And Society written by Anil Kumar Dhiman and has been published by Ess Ess Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-08-01 with Language Arts & Disciplines categories.

Introduction To Cataloging And Classification

DOWNLOAD
Author : Daniel N. Joudrey
language : en
Publisher: Bloomsbury Publishing USA
Release Date : 2015-09-29

Introduction To Cataloging And Classification written by Daniel N. Joudrey and has been published by Bloomsbury Publishing USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-29 with Language Arts & Disciplines categories.

A new edition of this best-selling textbook reintroduces the topic of library cataloging from a fresh, modern perspective. Not many books merit an eleventh edition, but this popular text does. Newly updated, Introduction to Cataloging and Classification provides an introduction to descriptive cataloging based on contemporary standards, explaining the basic tenets to readers without previous experience, as well as to those who merely want a better understanding of the process as it exists today. The text opens with the foundations of cataloging, then moves to specific details and subject matter such as Functional Requirements for Bibliographic Records (FRBR), Functional Requirements for Authority Data (FRAD), the International Cataloging Principles (ICP), and RDA. Unlike other texts, the book doesn't presume a close familiarity with the MARC bibliographic or authorities formats; ALA's Anglo-American Cataloging Rules, 2nd Edition, revised (AACR2R); or the International Standard Bibliographic Description (ISBD). Subject access to library materials is covered in sufficient depth to make the reader comfortable with the principles and practices of subject cataloging and classification. In addition, the book introduces MARC, BIBFRAME, and other approaches used to communicate and display bibliographic data. Discussions of formatting, presentation, and administrative issues complete the book; questions useful for review and study appear at the end of each chapter.

Step By Step Tutorials On Deep Learning Using Scikit Learn Keras And Tensorflow With Python Gui

DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-06-18

Step By Step Tutorials On Deep Learning Using Scikit Learn Keras And Tensorflow With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-18 with Computers categories.

In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on classifying fruits, classifying cats/dogs, detecting furnitures, and classifying fashion. In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fruits using Fruits 360 dataset provided by Kaggle (https://www.kaggle.com/moltean/fruits/code) using Transfer Learning and CNN models. You will build a GUI application for this purpose. Here's the outline of the steps, focusing on transfer learning: 1. Dataset Preparation: Download the Fruits 360 dataset from Kaggle. Extract the dataset files and organize them into appropriate folders for training and testing. Install the necessary libraries like TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, and NumPy; Data Preprocessing: Use OpenCV to read and load the fruit images from the dataset. Resize the images to a consistent size to feed them into the neural network. Convert the images to numerical arrays using NumPy. Normalize the image pixel values to a range between 0 and 1. Split the dataset into training and testing sets using Scikit-Learn. 3. Building the Model with Transfer Learning: Import the required modules from TensorFlow and Keras. Load a pre-trained model (e.g., VGG16, ResNet50, InceptionV3) without the top (fully connected) layers. Freeze the weights of the pre-trained layers to prevent them from being updated during training. Add your own fully connected layers on top of the pre-trained layers. Compile the model by specifying the loss function, optimizer, and evaluation metrics; 4. Model Training: Use the prepared training data to train the model. Specify the number of epochs and batch size for training. Monitor the training process for accuracy and loss using callbacks; 5. Model Evaluation: Evaluate the trained model on the test dataset using Scikit-Learn. Calculate accuracy, precision, recall, and F1-score for the classification results; 6. Predictions: Load and preprocess new fruit images for prediction using the same steps as in data preprocessing. Use the trained model to predict the class labels of the new images. In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying cats/dogs using dataset provided by Kaggle (https://www.kaggle.com/chetankv/dogs-cats-images) using Using CNN with Data Generator. You will build a GUI application for this purpose. The following steps are taken: Set up your development environment: Install the necessary libraries such as TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy, and any other dependencies required for the tutorial; Load and preprocess the dataset: Use libraries like OpenCV and NumPy to load and preprocess the dataset. Split the dataset into training and testing sets; Design and train the classification model: Use TensorFlow and Keras to design a convolutional neural network (CNN) model for image classification. Define the architecture of the model, compile it with an appropriate loss function and optimizer, and train it using the training dataset; Evaluate the model: Evaluate the trained model using the testing dataset. Calculate metrics such as accuracy, precision, recall, and F1 score to assess the model's performance; Make predictions: Use the trained model to make predictions on new unseen images. Preprocess the images, feed them into the model, and obtain the predicted class labels; Visualize the results: Use libraries like Matplotlib or OpenCV to visualize the results, such as displaying sample images with their predicted labels, plotting the training/validation loss and accuracy curves, and creating a confusion matrix. In Chapter 4, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting furnitures using Furniture Detector dataset provided by Kaggle (https://www.kaggle.com/akkithetechie/furniture-detector) using VGG16 model. You will build a GUI application for this purpose. Here are the steps you can follow to perform furniture detection: Dataset Preparation: Extract the dataset files and organize them into appropriate directories for training and testing; Data Preprocessing: Load the dataset using Pandas to analyze and preprocess the data. Explore the dataset to understand its structure, features, and labels. Perform any necessary preprocessing steps like resizing images, normalizing pixel values, and splitting the data into training and testing sets; Feature Extraction and Representation: Use OpenCV or any image processing libraries to extract meaningful features from the images. This might include techniques like edge detection, color-based features, or texture analysis. Convert the images and extracted features into a suitable representation for machine learning models. This can be achieved using NumPy arrays or other formats compatible with the chosen libraries; Model Training: Define a deep learning model using TensorFlow and Keras for furniture detection. You can choose pre-trained models like VGG16, ResNet, or custom architectures. Compile the model with an appropriate loss function, optimizer, and evaluation metrics. Train the model on the preprocessed dataset using the training set. Adjust hyperparameters like batch size, learning rate, and number of epochs to improve performance; Model Evaluation: Evaluate the trained model using the testing set. Calculate metrics such as accuracy, precision, recall, and F1 score to assess the model's performance. Analyze the results and identify areas for improvement; Model Deployment and Inference: Once satisfied with the model's performance, save it to disk for future use. Deploy the model to make predictions on new, unseen images. Use the trained model to perform furniture detection on images by applying it to the test set or new data. In Chapter 5, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fashion using Fashion MNIST dataset provided by Kaggle (https://www.kaggle.com/zalando-research/fashionmnist/code) using CNN model. You will build a GUI application for this purpose. Here are the general steps to implement image classification using the Fashion MNIST dataset: Import the necessary libraries: Import the required libraries such as TensorFlow, Keras, NumPy, Pandas, and Matplotlib for handling the dataset, building the model, and visualizing the results; Load and preprocess the dataset: Load the Fashion MNIST dataset, which consists of images of clothing items. Split the dataset into training and testing sets. Preprocess the images by scaling the pixel values to a range of 0 to 1 and converting the labels to categorical format; Define the model architecture: Create a convolutional neural network (CNN) model using Keras. The CNN consists of convolutional layers, pooling layers, and fully connected layers. Choose the appropriate architecture based on the complexity of the dataset; Compile the model: Specify the loss function, optimizer, and evaluation metric for the model. Common choices include categorical cross-entropy for multi-class classification and Adam optimizer; Train the model: Fit the model to the training data using the fit() function. Specify the number of epochs (iterations) and batch size. Monitor the training progress by tracking the loss and accuracy; Evaluate the model: Evaluate the trained model using the test dataset. Calculate the accuracy and other performance metrics to assess the model's performance; Make predictions: Use the trained model to make predictions on new unseen images. Load the test images, preprocess them, and pass them through the model to obtain class probabilities or predictions; Visualize the results: Visualize the training progress by plotting the loss and accuracy curves. Additionally, you can visualize the predictions and compare them with the true labels to gain insights into the model's performance.

Library Classification Trends In The 21st Century

DOWNLOAD
Author : Rajendra Kumbhar
language : en
Publisher: Elsevier
Release Date : 2011-11-18

Library Classification Trends In The 21st Century written by Rajendra Kumbhar and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-11-18 with Language Arts & Disciplines categories.

Library Classification Trends in the 21st Century traces development in and around library classification as reported in literature published in the first decade of the 21st century. It reviews literature published on various aspects of library classification, including modern applications of classification such as internet resource discovery, automatic book classification, text categorization, modern manifestations of classification such as taxonomies, folksonomies and ontologies and interoperable systems enabling crosswalk. The book also features classification education and an exploration of relevant topics. Covers all aspects of library classification It is the only book that reviews literature published over a decade’s time span (1999-2009) Well thought chapterization which is in tune with the LIS and classification curriculum

Learn Library Classification

Recent Posts