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H 22
Handbook of research on applied intelligence for health and clinical informatics / / Anuradha Thakare, Sanjeev Wagh, Manisha Bhende, Ahmed Anetr, and Xiao-Zhi Gao, editors. - Hershey, PA : : Medical Information Science Reference,, [2021]. - 1 online resource. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/C2275AC8-DFA2-40C4-BB10-FF0F01DF714B. - ISBN 9781799877103 (electronic book). - ISBN 1799877108 (electronic book). - ISBN 9781799877110 (electronic bk.). - ISBN 1799877116 (electronic bk.)
Description based on online resource; title from digital title page (viewed on January 07, 2022).
Параллельные издания: Print version: : Handbook of research on applied intelligence for health and clinical informatics. - Hershey, PA : Medical Information Science Reference, [2021]. - ISBN 9781799877097
Содержание:
Automated ICD Coding Using Deep Learning / Sagar Dhobale -- A Review on Social Distance & Face Mask Detector / Lokesh Giripunje, Arpita Patra, Riya Chaudhari, Aniket Sagar -- Decision Support Proposal for Imbalanced Clinical Data / Kevser Sahinbas -- Bone Tumor Detection Using Machine Learning / Deepak Mane -- A Novel Approach of Lung Tumor Segmentation using a 3D Deep Convolutional Neural Network / Shweta Tyagi, Sanjay Talbar, Abhishek Mahajan -- Ultrasonic Detection of Down Syndrome Using Multiscale Quantiser With Convolutional Neural Network / Michael Simon, Kavitha A.R. -- Detection of Lung Diseases Using Machine Learning and Deep Learning Techniques : An Analytical Study / Shravani Nimbolkar, Anuradha Thakare, Subhradeep Mitra, Omkar Biranje, Anant Sutar -- M2UNet++ A Modified Multi-Scale UNet++ Architecture for Automatic Liver Segmentation in Computed Tomography Images / Devidas Kushnure, Sanjay Talbar.
~РУБ DDC 610.285
Рубрики: Medical informatics--Methods.
Medical Informatics--methods
Artificial Intelligence
Health Information Management--methods
Аннотация: "This book focuses on the study of resources and methods for the management of healthcare infrastructure and information highlighting health and clinical data structure, behavior, and interactions of natural and engineered computational systems to helps researchers and practitioners learn further investigation and solutions"--
Доп.точки доступа:
Thakare, Anuradha, (1978-) \editor.\
Wagh, Sanjeev, \editor.\
Bhende, Manisha, (1977-) \editor.\
Anetr, Ahmed, (1974-) \editor.\
Gao, Xiao-Zhi, (1972-) \editor.\
H 22
Handbook of research on applied intelligence for health and clinical informatics / / Anuradha Thakare, Sanjeev Wagh, Manisha Bhende, Ahmed Anetr, and Xiao-Zhi Gao, editors. - Hershey, PA : : Medical Information Science Reference,, [2021]. - 1 online resource. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/C2275AC8-DFA2-40C4-BB10-FF0F01DF714B. - ISBN 9781799877103 (electronic book). - ISBN 1799877108 (electronic book). - ISBN 9781799877110 (electronic bk.). - ISBN 1799877116 (electronic bk.)
Description based on online resource; title from digital title page (viewed on January 07, 2022).
Параллельные издания: Print version: : Handbook of research on applied intelligence for health and clinical informatics. - Hershey, PA : Medical Information Science Reference, [2021]. - ISBN 9781799877097
Содержание:
Automated ICD Coding Using Deep Learning / Sagar Dhobale -- A Review on Social Distance & Face Mask Detector / Lokesh Giripunje, Arpita Patra, Riya Chaudhari, Aniket Sagar -- Decision Support Proposal for Imbalanced Clinical Data / Kevser Sahinbas -- Bone Tumor Detection Using Machine Learning / Deepak Mane -- A Novel Approach of Lung Tumor Segmentation using a 3D Deep Convolutional Neural Network / Shweta Tyagi, Sanjay Talbar, Abhishek Mahajan -- Ultrasonic Detection of Down Syndrome Using Multiscale Quantiser With Convolutional Neural Network / Michael Simon, Kavitha A.R. -- Detection of Lung Diseases Using Machine Learning and Deep Learning Techniques : An Analytical Study / Shravani Nimbolkar, Anuradha Thakare, Subhradeep Mitra, Omkar Biranje, Anant Sutar -- M2UNet++ A Modified Multi-Scale UNet++ Architecture for Automatic Liver Segmentation in Computed Tomography Images / Devidas Kushnure, Sanjay Talbar.
Рубрики: Medical informatics--Methods.
Medical Informatics--methods
Artificial Intelligence
Health Information Management--methods
Аннотация: "This book focuses on the study of resources and methods for the management of healthcare infrastructure and information highlighting health and clinical data structure, behavior, and interactions of natural and engineered computational systems to helps researchers and practitioners learn further investigation and solutions"--
Доп.точки доступа:
Thakare, Anuradha, (1978-) \editor.\
Wagh, Sanjeev, \editor.\
Bhende, Manisha, (1977-) \editor.\
Anetr, Ahmed, (1974-) \editor.\
Gao, Xiao-Zhi, (1972-) \editor.\
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L 62
Leveraging AI technologies for preventing and detecting sudden cardiac arrest and death / / Pradeep Nijalingappa, Sandeep Kautish, Mangesh Ghonge and Renjith Ravi, editors. - Hershey, PA : : Medical Information Science Reference,, [2022]. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/81FF1B71-9A66-4630-AF9E-072E34E7C091. - ISBN 1799884457. - ISBN 9781799884453 (electronic bk.)
Description based on print version record and CIP data provided by publisher; resource not viewed.
Параллельные издания: Print version: : Leveraging AI technologies for preventing and detecting sudden cardiac arrest and death. - Hershey, PA : Medical Information Science Reference, [2022]. - ISBN 9781799884439
~РУБ DDC 616.1/23
Рубрики: Artificial intelligence.
Heart Arrest--prevention & control
Death, Sudden, Cardiac--prevention & control
Artificial Intelligence
Decision Support Systems, Clinical
Intelligence artificielle.
artificial intelligence.
Artificial intelligence.
Аннотация: "This book discusses how AI technologies can assist physicians to make better clinical decisions enabling early detection of subclinical organ dysfunction, through the use of clinically relevant information that can be found in the massive amount of data and, thus, improving quality and efficiency of healthcare delivery"--
Доп.точки доступа:
Pradeep, Nijalingappa, (1977-) \editor.\
Kautish, Sandeep, (1981-) \editor.\
Ghonge, Mangesh, (1984-) \editor.\
Ravi, Renjith, (1985-) \editor.\
L 62
Leveraging AI technologies for preventing and detecting sudden cardiac arrest and death / / Pradeep Nijalingappa, Sandeep Kautish, Mangesh Ghonge and Renjith Ravi, editors. - Hershey, PA : : Medical Information Science Reference,, [2022]. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/81FF1B71-9A66-4630-AF9E-072E34E7C091. - ISBN 1799884457. - ISBN 9781799884453 (electronic bk.)
Description based on print version record and CIP data provided by publisher; resource not viewed.
Параллельные издания: Print version: : Leveraging AI technologies for preventing and detecting sudden cardiac arrest and death. - Hershey, PA : Medical Information Science Reference, [2022]. - ISBN 9781799884439
Рубрики: Artificial intelligence.
Heart Arrest--prevention & control
Death, Sudden, Cardiac--prevention & control
Artificial Intelligence
Decision Support Systems, Clinical
Intelligence artificielle.
artificial intelligence.
Artificial intelligence.
Аннотация: "This book discusses how AI technologies can assist physicians to make better clinical decisions enabling early detection of subclinical organ dysfunction, through the use of clinically relevant information that can be found in the massive amount of data and, thus, improving quality and efficiency of healthcare delivery"--
Доп.точки доступа:
Pradeep, Nijalingappa, (1977-) \editor.\
Kautish, Sandeep, (1981-) \editor.\
Ghonge, Mangesh, (1984-) \editor.\
Ravi, Renjith, (1985-) \editor.\
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B 76
Bonaccorso, Giuseppe.
Machine Learning Algorithms : : Popular Algorithms for Data Science and Machine Learning, 2nd Edition. / Giuseppe. Bonaccorso. - 2nd ed. - Birmingham : : Packt Publishing Ltd,, 2018. - 1 online resource (514 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/D9D16600-8F9C-45A4-B8EC-B980EF369FE7. - ISBN 9781789345483. - ISBN 1789345480
Introducing semi-supervised Support Vector Machines (S3VM). Print version record.
Параллельные издания: Print version: : Bonaccorso, Giuseppe. Machine Learning Algorithms : Popular Algorithms for Data Science and Machine Learning, 2nd Edition. - Birmingham : Packt Publishing Ltd, ©2018. - ISBN 9781789347999
Содержание:
Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: A Gentle Introduction to Machine Learning; Introduction -- classic and adaptive machines; Descriptive analysis; Predictive analysis; Only learning matters; Supervised learning; Unsupervised learning; Semi-supervised learning; Reinforcement learning; Computational neuroscience; Beyond machine learning -- deep learning and bio-inspired adaptive systems; Machine learning and big data; Summary; Chapter 2: Important Elements in Machine Learning; Data formats; Multiclass strategies.
One-vs-allOne-vs-one; Learnability; Underfitting and overfitting; Error measures and cost functions; PAC learning; Introduction to statistical learning concepts; MAP learning; Maximum likelihood learning; Class balancing; Resampling with replacement; SMOTE resampling; Elements of information theory; Entropy; Cross-entropy and mutual information ; Divergence measures between two probability distributions; Summary; Chapter 3: Feature Selection and Feature Engineering; scikit-learn toy datasets; Creating training and test sets; Managing categorical data; Managing missing features.
Data scaling and normalizationWhitening; Feature selection and filtering; Principal Component Analysis; Non-Negative Matrix Factorization; Sparse PCA; Kernel PCA; Independent Component Analysis; Atom extraction and dictionary learning; Visualizing high-dimensional datasets using t-SNE; Summary; Chapter 4: Regression Algorithms; Linear models for regression; A bidimensional example; Linear regression with scikit-learn and higher dimensionality; R2 score; Explained variance; Regressor analytic expression; Ridge, Lasso, and ElasticNet; Ridge; Lasso; ElasticNet; Robust regression; RANSAC.
Huber regressionBayesian regression; Polynomial regression; Isotonic regression; Summary; Chapter 5: Linear Classification Algorithms; Linear classification; Logistic regression; Implementation and optimizations; Stochastic gradient descent algorithms; Passive-aggressive algorithms; Passive-aggressive regression; Finding the optimal hyperparameters through a grid search; Classification metrics; Confusion matrix; Precision; Recall; F-Beta; Cohen's Kappa; Global classification report; Learning curve; ROC curve; Summary; Chapter 6: Naive Bayes and Discriminant Analysis; Bayes' theorem.
~РУБ DDC 006.31
Рубрики: Computers--Intelligence (AI) & Semantics.
Computers--Data Modeling & Design.
Database design & theory.
Artificial intelligence.
Machine learning.
Information architecture.
Computers--Machine Theory.
Mathematical theory of computation.
Machine learning.
Computer algorithms.
Computer algorithms.
Machine learning.
Аннотация: Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. This book will act as an entry point for anyone who wants to make a career in Machine Learning. It covers algorithms like Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, and Feature engineering.
B 76
Bonaccorso, Giuseppe.
Machine Learning Algorithms : : Popular Algorithms for Data Science and Machine Learning, 2nd Edition. / Giuseppe. Bonaccorso. - 2nd ed. - Birmingham : : Packt Publishing Ltd,, 2018. - 1 online resource (514 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/D9D16600-8F9C-45A4-B8EC-B980EF369FE7. - ISBN 9781789345483. - ISBN 1789345480
Introducing semi-supervised Support Vector Machines (S3VM). Print version record.
Параллельные издания: Print version: : Bonaccorso, Giuseppe. Machine Learning Algorithms : Popular Algorithms for Data Science and Machine Learning, 2nd Edition. - Birmingham : Packt Publishing Ltd, ©2018. - ISBN 9781789347999
Содержание:
Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: A Gentle Introduction to Machine Learning; Introduction -- classic and adaptive machines; Descriptive analysis; Predictive analysis; Only learning matters; Supervised learning; Unsupervised learning; Semi-supervised learning; Reinforcement learning; Computational neuroscience; Beyond machine learning -- deep learning and bio-inspired adaptive systems; Machine learning and big data; Summary; Chapter 2: Important Elements in Machine Learning; Data formats; Multiclass strategies.
One-vs-allOne-vs-one; Learnability; Underfitting and overfitting; Error measures and cost functions; PAC learning; Introduction to statistical learning concepts; MAP learning; Maximum likelihood learning; Class balancing; Resampling with replacement; SMOTE resampling; Elements of information theory; Entropy; Cross-entropy and mutual information ; Divergence measures between two probability distributions; Summary; Chapter 3: Feature Selection and Feature Engineering; scikit-learn toy datasets; Creating training and test sets; Managing categorical data; Managing missing features.
Data scaling and normalizationWhitening; Feature selection and filtering; Principal Component Analysis; Non-Negative Matrix Factorization; Sparse PCA; Kernel PCA; Independent Component Analysis; Atom extraction and dictionary learning; Visualizing high-dimensional datasets using t-SNE; Summary; Chapter 4: Regression Algorithms; Linear models for regression; A bidimensional example; Linear regression with scikit-learn and higher dimensionality; R2 score; Explained variance; Regressor analytic expression; Ridge, Lasso, and ElasticNet; Ridge; Lasso; ElasticNet; Robust regression; RANSAC.
Huber regressionBayesian regression; Polynomial regression; Isotonic regression; Summary; Chapter 5: Linear Classification Algorithms; Linear classification; Logistic regression; Implementation and optimizations; Stochastic gradient descent algorithms; Passive-aggressive algorithms; Passive-aggressive regression; Finding the optimal hyperparameters through a grid search; Classification metrics; Confusion matrix; Precision; Recall; F-Beta; Cohen's Kappa; Global classification report; Learning curve; ROC curve; Summary; Chapter 6: Naive Bayes and Discriminant Analysis; Bayes' theorem.
Рубрики: Computers--Intelligence (AI) & Semantics.
Computers--Data Modeling & Design.
Database design & theory.
Artificial intelligence.
Machine learning.
Information architecture.
Computers--Machine Theory.
Mathematical theory of computation.
Machine learning.
Computer algorithms.
Computer algorithms.
Machine learning.
Аннотация: Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. This book will act as an entry point for anyone who wants to make a career in Machine Learning. It covers algorithms like Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, and Feature engineering.
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S 17
Saleh, Hyatt,.
Machine learning fundamentals : : Use Python and scikit-learn to get up and running with the hottest developments in machine learning / / Hyatt Saleh. - Birmingham : : Packt Publishing,, ©2018. - 1 online resource (240 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/CE938EA4-6316-42FD-8CB4-3B924C055972. - ISBN 1789801761 (electronic bk.). - ISBN 9781789801767 (electronic bk.)
Description based upon print version of record. Supervised Learning Algorithms: Predict Annual Income
Параллельные издания: Print version: : Saleh, Hyatt Machine Learning Fundamentals : Use Python and Scikit-Learn to Get up and Running with the Hottest Developments in Machine Learning. - Birmingham : Packt Publishing Ltd,c2018. - ISBN 9781789803556
Содержание:
Intro; Preface; Introduction to Scikit-Learn; Introduction; Scikit-Learn; Advantages of Scikit-Learn; Disadvantages of Scikit-Learn; Data Representation; Tables of Data; Features and Target Matrices; Exercise 1: Loading a Sample Dataset and Creating the Features and Target Matrices; Activity 1: Selecting a Target Feature and Creating a Target Matrix; Data Preprocessing; Messy Data; Exercise 2: Dealing with Messy Data; Dealing with Categorical Features; Exercise 3: Applying Feature Engineering over Text Data; Rescaling Data; Exercise 4: Normalizing and Standardizing Data
Activity 2: Preprocessing an Entire DatasetScikit-Learn API; How Does It Work?; Supervised and Unsupervised Learning; Supervised Learning; Unsupervised Learning; Summary; Unsupervised Learning: Real-Life Applications; Introduction; Clustering; Clustering Types; Applications of Clustering; Exploring a Dataset: Wholesale Customers Dataset; Understanding the Dataset; Data Visualization; Loading the Dataset Using Pandas; Visualization Tools; Exercise 5: Plotting a Histogram of One Feature from the Noisy Circles Dataset; Activity 3: Using Data Visualization to Aid the Preprocessing Process
K-means AlgorithmUnderstanding the Algorithm; Exercise 6: Importing and Training the k-means Algorithm over a Dataset; Activity 4: Applying the k-means Algorithm to a Dataset; Mean-Shift Algorithm; Understanding the Algorithm; Exercise 7: Importing and Training the Mean-Shift Algorithm over a Dataset; Activity 5: Applying the Mean-Shift Algorithm to a Dataset; DBSCAN Algorithm; Understanding the Algorithm; Exercise 8: Importing and Training the DBSCAN Algorithm over a Dataset; Activity 6: Applying the DBSCAN Algorithm to the Dataset; Evaluating the Performance of Clusters
Available Metrics in Scikit-LearnExercise 9: Evaluating the Silhouette Coefficient Score and Calinski-Harabasz Index; Activity 7: Measuring and Comparing the Performance of the Algorithms; Summary; Supervised Learning: Key Steps; Introduction; Model Validation and Testing; Data Partition; Split Ratio; Exercise 10: Performing Data Partition over a Sample Dataset; Cross Validation; Exercise 11: Using Cross-Validation to Partition the Train Set into a Training and a Validation Set; Activity 8: Data Partition over a Handwritten Digit Dataset; Evaluation Metrics
Evaluation Metrics for Classification TasksExercise 12: Calculating Different Evaluation Metrics over a Classification Task; Choosing an Evaluation Metric; Evaluation Metrics for Regression Tasks; Exercise 13: Calculating Evaluation Metrics over a Regression Task; Activity 9: Evaluating the Performance of the Model Trained over a Handwritten Dataset; Error Analysis; Bias, Variance, and Data Mismatch; Exercise 14: Calculating the Error Rate over Different Sets of Data; Activity 10: Performing Error Analysis over a Model Trained to Recognize Handwritten Digits; Summary
~РУБ DDC 005.133
Рубрики: Python (Computer program language)
Machine learning.
Artificial intelligence.
COMPUTERS / Programming Languages / Python.
Аннотация: As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains the scikit-learn API, which is a package created to facilitate the process of building machine learning applications. By explaining the differences between supervised and unsupervised models and by ...
S 17
Saleh, Hyatt,.
Machine learning fundamentals : : Use Python and scikit-learn to get up and running with the hottest developments in machine learning / / Hyatt Saleh. - Birmingham : : Packt Publishing,, ©2018. - 1 online resource (240 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/CE938EA4-6316-42FD-8CB4-3B924C055972. - ISBN 1789801761 (electronic bk.). - ISBN 9781789801767 (electronic bk.)
Description based upon print version of record. Supervised Learning Algorithms: Predict Annual Income
Параллельные издания: Print version: : Saleh, Hyatt Machine Learning Fundamentals : Use Python and Scikit-Learn to Get up and Running with the Hottest Developments in Machine Learning. - Birmingham : Packt Publishing Ltd,c2018. - ISBN 9781789803556
Содержание:
Intro; Preface; Introduction to Scikit-Learn; Introduction; Scikit-Learn; Advantages of Scikit-Learn; Disadvantages of Scikit-Learn; Data Representation; Tables of Data; Features and Target Matrices; Exercise 1: Loading a Sample Dataset and Creating the Features and Target Matrices; Activity 1: Selecting a Target Feature and Creating a Target Matrix; Data Preprocessing; Messy Data; Exercise 2: Dealing with Messy Data; Dealing with Categorical Features; Exercise 3: Applying Feature Engineering over Text Data; Rescaling Data; Exercise 4: Normalizing and Standardizing Data
Activity 2: Preprocessing an Entire DatasetScikit-Learn API; How Does It Work?; Supervised and Unsupervised Learning; Supervised Learning; Unsupervised Learning; Summary; Unsupervised Learning: Real-Life Applications; Introduction; Clustering; Clustering Types; Applications of Clustering; Exploring a Dataset: Wholesale Customers Dataset; Understanding the Dataset; Data Visualization; Loading the Dataset Using Pandas; Visualization Tools; Exercise 5: Plotting a Histogram of One Feature from the Noisy Circles Dataset; Activity 3: Using Data Visualization to Aid the Preprocessing Process
K-means AlgorithmUnderstanding the Algorithm; Exercise 6: Importing and Training the k-means Algorithm over a Dataset; Activity 4: Applying the k-means Algorithm to a Dataset; Mean-Shift Algorithm; Understanding the Algorithm; Exercise 7: Importing and Training the Mean-Shift Algorithm over a Dataset; Activity 5: Applying the Mean-Shift Algorithm to a Dataset; DBSCAN Algorithm; Understanding the Algorithm; Exercise 8: Importing and Training the DBSCAN Algorithm over a Dataset; Activity 6: Applying the DBSCAN Algorithm to the Dataset; Evaluating the Performance of Clusters
Available Metrics in Scikit-LearnExercise 9: Evaluating the Silhouette Coefficient Score and Calinski-Harabasz Index; Activity 7: Measuring and Comparing the Performance of the Algorithms; Summary; Supervised Learning: Key Steps; Introduction; Model Validation and Testing; Data Partition; Split Ratio; Exercise 10: Performing Data Partition over a Sample Dataset; Cross Validation; Exercise 11: Using Cross-Validation to Partition the Train Set into a Training and a Validation Set; Activity 8: Data Partition over a Handwritten Digit Dataset; Evaluation Metrics
Evaluation Metrics for Classification TasksExercise 12: Calculating Different Evaluation Metrics over a Classification Task; Choosing an Evaluation Metric; Evaluation Metrics for Regression Tasks; Exercise 13: Calculating Evaluation Metrics over a Regression Task; Activity 9: Evaluating the Performance of the Model Trained over a Handwritten Dataset; Error Analysis; Bias, Variance, and Data Mismatch; Exercise 14: Calculating the Error Rate over Different Sets of Data; Activity 10: Performing Error Analysis over a Model Trained to Recognize Handwritten Digits; Summary
Рубрики: Python (Computer program language)
Machine learning.
Artificial intelligence.
COMPUTERS / Programming Languages / Python.
Аннотация: As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains the scikit-learn API, which is a package created to facilitate the process of building machine learning applications. By explaining the differences between supervised and unsupervised models and by ...
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G 42
Ghosh, Sohom.
Natural Language Processing Fundamentals [[electronic resource] :] : Build Intelligent Applications That Can Interpret the Human Language to Deliver Impactful Results. / Sohom. Ghosh, Gunning, Dwight. - Birmingham : : Packt Publishing Ltd,, 2019. - 1 online resource (375 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/777E904C-09AF-4DC7-9EC4-175467B6DA0D. - ISBN 178995598X. - ISBN 9781789955989 (electronic bk.)
Description based upon print version of record.
Параллельные издания: Print version: : Ghosh, Sohom Natural Language Processing Fundamentals : Build Intelligent Applications That Can Interpret the Human Language to Deliver Impactful Results. - Birmingham : Packt Publishing Ltd,c2019. - ISBN 9781789954043
~РУБ DDC 006.35
Рубрики: Natural language processing (Computer science)
Computational linguistics.
Artificial intelligence.
COMPUTERS / General
Аннотация: Natural Language Processing Fundamentals starts with basics and goes on to explain various NLP tools and techniques that equip you with all that you need to solve common business problems for processing text.
Доп.точки доступа:
Gunning, Dwight.
G 42
Ghosh, Sohom.
Natural Language Processing Fundamentals [[electronic resource] :] : Build Intelligent Applications That Can Interpret the Human Language to Deliver Impactful Results. / Sohom. Ghosh, Gunning, Dwight. - Birmingham : : Packt Publishing Ltd,, 2019. - 1 online resource (375 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/777E904C-09AF-4DC7-9EC4-175467B6DA0D. - ISBN 178995598X. - ISBN 9781789955989 (electronic bk.)
Description based upon print version of record.
Параллельные издания: Print version: : Ghosh, Sohom Natural Language Processing Fundamentals : Build Intelligent Applications That Can Interpret the Human Language to Deliver Impactful Results. - Birmingham : Packt Publishing Ltd,c2019. - ISBN 9781789954043
Рубрики: Natural language processing (Computer science)
Computational linguistics.
Artificial intelligence.
COMPUTERS / General
Аннотация: Natural Language Processing Fundamentals starts with basics and goes on to explain various NLP tools and techniques that equip you with all that you need to solve common business problems for processing text.
Доп.точки доступа:
Gunning, Dwight.
26.
Подробнее
J 41
Jennings, Charles, (1948-).
Artificial Intelligence : : rise of the lightspeed learners / / Charles Jennings. - Lanham : : Rowman & Littlefield, an imprint of The Rowman & Littlefield Publishing Group, Inc.,, [2019]. - 1 online resource. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/751B5C9F-E56A-436E-980B-A1E8DA47B743. - ISBN 1538116812. - ISBN 9781538116814 (electronic bk.)
Description based on print version record and CIP data provided by publisher; resource not viewed.
Параллельные издания: Print version: : Jennings, Charles, 1948 author. Artificial Intelligence. - Lanham : Rowman & Littlefield, an imprint of The Rowman & Littlefield Publishing Group, Inc., [2019]. - ISBN 9781538116807
Содержание:
An uncanny ability to learn -- Not your father's AI -- The singularity games -- Truckin' in flip flops -- Ben Franklin's purse -- A modest proposal -- Uncle Sam vs. Red Star -- The porn star's deepfake, and other security paradoxes -- Ais in the government henhouse -- The AI casino -- Artificially Intelligent poetry? -- The way forward.
~РУБ DDC 006.3
Рубрики: Artificial intelligence.
Artificial intelligence.
COMPUTERS / General
J 41
Jennings, Charles, (1948-).
Artificial Intelligence : : rise of the lightspeed learners / / Charles Jennings. - Lanham : : Rowman & Littlefield, an imprint of The Rowman & Littlefield Publishing Group, Inc.,, [2019]. - 1 online resource. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/751B5C9F-E56A-436E-980B-A1E8DA47B743. - ISBN 1538116812. - ISBN 9781538116814 (electronic bk.)
Description based on print version record and CIP data provided by publisher; resource not viewed.
Параллельные издания: Print version: : Jennings, Charles, 1948 author. Artificial Intelligence. - Lanham : Rowman & Littlefield, an imprint of The Rowman & Littlefield Publishing Group, Inc., [2019]. - ISBN 9781538116807
Содержание:
An uncanny ability to learn -- Not your father's AI -- The singularity games -- Truckin' in flip flops -- Ben Franklin's purse -- A modest proposal -- Uncle Sam vs. Red Star -- The porn star's deepfake, and other security paradoxes -- Ais in the government henhouse -- The AI casino -- Artificially Intelligent poetry? -- The way forward.
Рубрики: Artificial intelligence.
Artificial intelligence.
COMPUTERS / General
27.
Подробнее
A 81
Artasanchez, Alberto,.
Artificial intelligence with Python : : your complete guide to building intelligent apps using Python 3.x and TensorFlow 2 / / Alberto Artasanchez, Prateek Joshi. - Second edition. - Birmingham : : Packt Publishing,, ©2020. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/B5042755-A65D-468B-9D50-C8E1CDBF6ABE. - ISBN 1839216077 (electronic bk.). - ISBN 9781839216077 (electronic bk.)
Includes index. Online resource ; title from PDF title page (EBSCO, viewed March 17, 2020).
~РУБ DDC 005.13/3
Рубрики: Python (Computer program language)
Artificial intelligence--Data processing.
Application software--Development.
Доп.точки доступа:
Joshi, Prateek, \author.\
A 81
Artasanchez, Alberto,.
Artificial intelligence with Python : : your complete guide to building intelligent apps using Python 3.x and TensorFlow 2 / / Alberto Artasanchez, Prateek Joshi. - Second edition. - Birmingham : : Packt Publishing,, ©2020. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/B5042755-A65D-468B-9D50-C8E1CDBF6ABE. - ISBN 1839216077 (electronic bk.). - ISBN 9781839216077 (electronic bk.)
Includes index. Online resource ; title from PDF title page (EBSCO, viewed March 17, 2020).
Рубрики: Python (Computer program language)
Artificial intelligence--Data processing.
Application software--Development.
Доп.точки доступа:
Joshi, Prateek, \author.\
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Подробнее
K 83
Krishnakumar, Arunkumar,.
Quantum computing and Blockchain in business : : exploring the applications, challenges, and collision of quantum computing and blockchain / / Arunkumar Krishnakumar. - Birmingham, UK : : Packt Publishing,, 2020. - 1 online resource (1 volume) : : il. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/58D30AF0-02FA-4E32-9F42-E5F6D4682ECC. - ISBN 1838646132. - ISBN 9781838646134 (electronic bk.)
Description based on online resource; title from title page (Safari, viewed September 3, 2020).
Содержание:
Table of ContentsIntroduction to Quantum Computing and BlockchainQuantum Computing - Key Discussion PointsThe Data EconomyThe Impact on Financial ServicesInterview with Dr. Dave Snelling, Fujitsu FellowThe Impact on Healthcare and PharmaInterview with Dr. B. Rajathilagam, Head of AI Research, Amrita Vishwa VidyapeethamThe Impact on GovernanceInterview with Max Henderson, Senior Data Scientist, Rigetti and QxBranchThe Impact on Smart Cities and EnvironmentInterview with Sam McArdle, Quantum Computing Researcher at the University of OxfordThe Impact on ChemistryThe Impact on Logistics(N.B. Please use the Look Inside option to see further chapters).
~РУБ DDC 658.514
Рубрики: Quantum computing.
Blockchains (Databases)
Information technology--Management.
Technological innovations--Management.
Coding theory & cryptology.
Data encryption.
Artificial intelligence.
Machine learning.
Mathematical theory of computation.
Computers--Intelligence (AI) & Semantics.
Computers--Security--Cryptography.
Computers--Machine Theory.
Blockchains (Databases)
Information technology--Management
Quantum computing
Technological innovations--Management
K 83
Krishnakumar, Arunkumar,.
Quantum computing and Blockchain in business : : exploring the applications, challenges, and collision of quantum computing and blockchain / / Arunkumar Krishnakumar. - Birmingham, UK : : Packt Publishing,, 2020. - 1 online resource (1 volume) : : il. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/58D30AF0-02FA-4E32-9F42-E5F6D4682ECC. - ISBN 1838646132. - ISBN 9781838646134 (electronic bk.)
Description based on online resource; title from title page (Safari, viewed September 3, 2020).
Содержание:
Table of ContentsIntroduction to Quantum Computing and BlockchainQuantum Computing - Key Discussion PointsThe Data EconomyThe Impact on Financial ServicesInterview with Dr. Dave Snelling, Fujitsu FellowThe Impact on Healthcare and PharmaInterview with Dr. B. Rajathilagam, Head of AI Research, Amrita Vishwa VidyapeethamThe Impact on GovernanceInterview with Max Henderson, Senior Data Scientist, Rigetti and QxBranchThe Impact on Smart Cities and EnvironmentInterview with Sam McArdle, Quantum Computing Researcher at the University of OxfordThe Impact on ChemistryThe Impact on Logistics(N.B. Please use the Look Inside option to see further chapters).
Рубрики: Quantum computing.
Blockchains (Databases)
Information technology--Management.
Technological innovations--Management.
Coding theory & cryptology.
Data encryption.
Artificial intelligence.
Machine learning.
Mathematical theory of computation.
Computers--Intelligence (AI) & Semantics.
Computers--Security--Cryptography.
Computers--Machine Theory.
Blockchains (Databases)
Information technology--Management
Quantum computing
Technological innovations--Management
29.
Подробнее
F 25
Farrell, Peter, (1966-).
The statistics and calculus workshop : a comprehensive introduction to mathematics in Python for artificial intelligence applications / / Peter Farrell [and five others]. - Birmingham : : Packt Publishing,, 2020. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/5349CE3F-94F3-473E-94B8-E891ED2935F4. - ISBN 9781800208360 (electronic book). - ISBN 1800208367 (electronic book)
Параллельные издания: Print version: :
Содержание:
Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Fundamentals of Python -- Introduction -- Control Flow Methods -- if Statements -- Exercise 1.01: Divisibility with Conditionals -- Loops -- The while Loop -- The for Loop -- Exercise 1.02: Number Guessing Game -- Data Structures -- Strings -- Lists -- Exercise 1.03: Multi-Dimensional Lists -- Tuples -- Sets -- Dictionaries -- Exercise 1.04: Shopping Cart Calculations -- Functions and Algorithms -- Functions -- Exercise 1.05: Finding the Maximum -- Recursion -- Exercise 1.06: The Tower of Hanoi -- Algorithm Design
Exercise 1.07: The N-Queens Problem -- Testing, Debugging, and Version Control -- Testing -- Debugging -- Exercise 1.08: Testing for Concurrency -- Version Control -- Exercise 1.09: Version Control with Git and GitHub -- Activity 1.01: Building a Sudoku Solver -- Summary -- Chapter 2: Python's Main Tools for Statistics -- Introduction -- Scientific Computing and NumPy Basics -- NumPy Arrays -- Vectorization -- Exercise 2.01: Timing Vectorized Operations in NumPy -- Random Sampling -- Working with Tabular Data in pandas -- Initializing a DataFrame Object -- Accessing Rows and Columns
Manipulating DataFrames -- Exercise 2.02: Data Table Manipulation -- Advanced Pandas Functionalities -- Exercise 2.03: The Student Dataset -- Data Visualization with Matplotlib and Seaborn -- Scatter Plots -- Line Graphs -- Bar Graphs -- Histograms -- Heatmaps -- Exercise 2.04: Visualization of Probability Distributions -- Visualization Shorthand from Seaborn and Pandas -- Activity 2.01: Analyzing the Communities and Crime Dataset -- Summary -- Chapter 3: Python's Statistical Toolbox -- Introduction -- An Overview of Statistics -- Types of Data in Statistics -- Categorical Data
Exercise 3.01: Visualizing Weather Percentages -- Numerical Data -- Exercise 3.02: Min-Max Scaling -- Ordinal Data -- Descriptive Statistics -- Central Tendency -- Dispersion -- Exercise 3.03: Visualizing Probability Density Functions -- Python-Related Descriptive Statistics -- Inferential Statistics -- T-Tests -- Correlation Matrix -- Exercise 3.04: Identifying and Testing Equality of Means -- Statistical and Machine Learning Models -- Exercise 3.05: Model Selection -- Python's Other Statistics Tools -- Activity 3.01: Revisiting the Communities and Crimes Dataset -- Summary
Chapter 4: Functions and Algebra with Python -- Introduction -- Functions -- Common Functions -- Domain and Range -- Function Roots and Equations -- The Plot of a Function -- Exercise 4.01: Function Identification from Plots -- Function Transformations -- Shifts -- Scaling -- Exercise 4.02: Function Transformation Identification -- Equations -- Algebraic Manipulations -- Factoring -- Using Python -- Exercise 4.03: Introduction to Break-Even Analysis -- Systems of Equations -- Systems of Linear Equations -- Exercise 4.04: Matrix Solution with NumPy -- Systems of Non-Linear Equations
~РУБ DDC 006.3015
Рубрики: Artificial intelligence--Mathematics.
Python (Computer program language)
Intelligence artificielle--Mathématiques.
Python (Langage de programmation)
Artificial intelligence--Mathematics
Python (Computer program language)
Аннотация: With examples and activities that help you achieve real results, applying calculus and statistical methods relevant to advanced data science has never been so easy Key Features Discover how most programmers use the main Python libraries when performing statistics with Python Use descriptive statistics and visualizations to answer business and scientific questions Solve complicated calculus problems, such as arc length and solids of revolution using derivatives and integrals Book Description Are you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python. The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions. By the end of this book, you'll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges. What you will learn Get to grips with the fundamental mathematical functions in Python Perform calculations on tabular datasets using pandas Understand the differences between polynomials, rational functions, exponential functions, and trigonometric functions Use algebra techniques for solving systems of equations Solve real-world problems with probability Solve optimization problems with derivatives and integrals Who this book is for If you are a Python programmer who wants to develop intelligent solutions that solve challenging business problems, then this book is for you. To better grasp the concepts explained in this book, you must have a thorough understanding of advanced mathematical concepts, such as Markov chains, Euler's formula, and Runge-Kutta methods as the book only explains how these techniques and concepts can be implemented in Py...
F 25
Farrell, Peter, (1966-).
The statistics and calculus workshop : a comprehensive introduction to mathematics in Python for artificial intelligence applications / / Peter Farrell [and five others]. - Birmingham : : Packt Publishing,, 2020. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/5349CE3F-94F3-473E-94B8-E891ED2935F4. - ISBN 9781800208360 (electronic book). - ISBN 1800208367 (electronic book)
Параллельные издания: Print version: :
Содержание:
Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Fundamentals of Python -- Introduction -- Control Flow Methods -- if Statements -- Exercise 1.01: Divisibility with Conditionals -- Loops -- The while Loop -- The for Loop -- Exercise 1.02: Number Guessing Game -- Data Structures -- Strings -- Lists -- Exercise 1.03: Multi-Dimensional Lists -- Tuples -- Sets -- Dictionaries -- Exercise 1.04: Shopping Cart Calculations -- Functions and Algorithms -- Functions -- Exercise 1.05: Finding the Maximum -- Recursion -- Exercise 1.06: The Tower of Hanoi -- Algorithm Design
Exercise 1.07: The N-Queens Problem -- Testing, Debugging, and Version Control -- Testing -- Debugging -- Exercise 1.08: Testing for Concurrency -- Version Control -- Exercise 1.09: Version Control with Git and GitHub -- Activity 1.01: Building a Sudoku Solver -- Summary -- Chapter 2: Python's Main Tools for Statistics -- Introduction -- Scientific Computing and NumPy Basics -- NumPy Arrays -- Vectorization -- Exercise 2.01: Timing Vectorized Operations in NumPy -- Random Sampling -- Working with Tabular Data in pandas -- Initializing a DataFrame Object -- Accessing Rows and Columns
Manipulating DataFrames -- Exercise 2.02: Data Table Manipulation -- Advanced Pandas Functionalities -- Exercise 2.03: The Student Dataset -- Data Visualization with Matplotlib and Seaborn -- Scatter Plots -- Line Graphs -- Bar Graphs -- Histograms -- Heatmaps -- Exercise 2.04: Visualization of Probability Distributions -- Visualization Shorthand from Seaborn and Pandas -- Activity 2.01: Analyzing the Communities and Crime Dataset -- Summary -- Chapter 3: Python's Statistical Toolbox -- Introduction -- An Overview of Statistics -- Types of Data in Statistics -- Categorical Data
Exercise 3.01: Visualizing Weather Percentages -- Numerical Data -- Exercise 3.02: Min-Max Scaling -- Ordinal Data -- Descriptive Statistics -- Central Tendency -- Dispersion -- Exercise 3.03: Visualizing Probability Density Functions -- Python-Related Descriptive Statistics -- Inferential Statistics -- T-Tests -- Correlation Matrix -- Exercise 3.04: Identifying and Testing Equality of Means -- Statistical and Machine Learning Models -- Exercise 3.05: Model Selection -- Python's Other Statistics Tools -- Activity 3.01: Revisiting the Communities and Crimes Dataset -- Summary
Chapter 4: Functions and Algebra with Python -- Introduction -- Functions -- Common Functions -- Domain and Range -- Function Roots and Equations -- The Plot of a Function -- Exercise 4.01: Function Identification from Plots -- Function Transformations -- Shifts -- Scaling -- Exercise 4.02: Function Transformation Identification -- Equations -- Algebraic Manipulations -- Factoring -- Using Python -- Exercise 4.03: Introduction to Break-Even Analysis -- Systems of Equations -- Systems of Linear Equations -- Exercise 4.04: Matrix Solution with NumPy -- Systems of Non-Linear Equations
Рубрики: Artificial intelligence--Mathematics.
Python (Computer program language)
Intelligence artificielle--Mathématiques.
Python (Langage de programmation)
Artificial intelligence--Mathematics
Python (Computer program language)
Аннотация: With examples and activities that help you achieve real results, applying calculus and statistical methods relevant to advanced data science has never been so easy Key Features Discover how most programmers use the main Python libraries when performing statistics with Python Use descriptive statistics and visualizations to answer business and scientific questions Solve complicated calculus problems, such as arc length and solids of revolution using derivatives and integrals Book Description Are you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python. The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions. By the end of this book, you'll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges. What you will learn Get to grips with the fundamental mathematical functions in Python Perform calculations on tabular datasets using pandas Understand the differences between polynomials, rational functions, exponential functions, and trigonometric functions Use algebra techniques for solving systems of equations Solve real-world problems with probability Solve optimization problems with derivatives and integrals Who this book is for If you are a Python programmer who wants to develop intelligent solutions that solve challenging business problems, then this book is for you. To better grasp the concepts explained in this book, you must have a thorough understanding of advanced mathematical concepts, such as Markov chains, Euler's formula, and Runge-Kutta methods as the book only explains how these techniques and concepts can be implemented in Py...
30.
Подробнее
I 69
Internet of Things and machine learning in agriculture : : technological impacts and challenges / / edited by Vishal Jain, Jyotir Moy Chatterjee, Abhishek Kumar, Pramod Singh Rathore. - Berlin ; ; Boston : : Walter de Gruyter GmbH,, 2021. - 1 online resource (xvi, 410 pages) : : il. - (De Gruyter frontiers in computational intelligence ; ; volume 8). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/50820268-35AC-4432-8B7B-39A469CF486C. - ISBN 9783110691276 (electronic bk. ;. - ISBN 3110691272 (electronic bk. ;. - ISBN 9783110691283 (electronic bk. ;. - ISBN 3110691280 (electronic bk. ;
Print version of record.
Параллельные издания: Print version: : Chatterjee, Jyotir Moy Internet of Things and machine learning in agriculture. - Berlin/Boston : Walter de Gruyter GmbH,c2021
~РУБ DDC 338.102854678
Рубрики: Agriculture--Data processing.
Internet of things.
Artificial intelligence--Agricultural applications.
Machine learning.
Agriculture--Informatique.
Internet des objets.
Intelligence artificielle--Applications agricoles.
Apprentissage automatique.
COMPUTERS / Information Technology.
Agriculture--Data processing.
Internet of things.
Electronic books.
Аннотация: Agriculture is one of the most fundamental human activities. As the farming capacity has expanded, the usage of resources such as land, fertilizer, and water has grown exponentially, and environmental pressures from modern farming techniques have stressed natural landscapes. Still, by some estimates, worldwide food production needs to increase to keep up with global food demand. 'Machine Learning and the Internet of Things' can play a promising role in the Agricultural industry, and help to increase food production while respecting the environment. This book explains how these technologies can be applied, offering many case studies developed in the research world.
Доп.точки доступа:
Jain, Vishal, (1983-) \editor.\
Chatterjee, Jyotir Moy, \editor.\
Kumar, Abhishek, \editor.\
Rathore, Pramod Singh, (1988-) \editor.\
I 69
Internet of Things and machine learning in agriculture : : technological impacts and challenges / / edited by Vishal Jain, Jyotir Moy Chatterjee, Abhishek Kumar, Pramod Singh Rathore. - Berlin ; ; Boston : : Walter de Gruyter GmbH,, 2021. - 1 online resource (xvi, 410 pages) : : il. - (De Gruyter frontiers in computational intelligence ; ; volume 8). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/50820268-35AC-4432-8B7B-39A469CF486C. - ISBN 9783110691276 (electronic bk. ;. - ISBN 3110691272 (electronic bk. ;. - ISBN 9783110691283 (electronic bk. ;. - ISBN 3110691280 (electronic bk. ;
Print version of record.
Параллельные издания: Print version: : Chatterjee, Jyotir Moy Internet of Things and machine learning in agriculture. - Berlin/Boston : Walter de Gruyter GmbH,c2021
Рубрики: Agriculture--Data processing.
Internet of things.
Artificial intelligence--Agricultural applications.
Machine learning.
Agriculture--Informatique.
Internet des objets.
Intelligence artificielle--Applications agricoles.
Apprentissage automatique.
COMPUTERS / Information Technology.
Agriculture--Data processing.
Internet of things.
Electronic books.
Аннотация: Agriculture is one of the most fundamental human activities. As the farming capacity has expanded, the usage of resources such as land, fertilizer, and water has grown exponentially, and environmental pressures from modern farming techniques have stressed natural landscapes. Still, by some estimates, worldwide food production needs to increase to keep up with global food demand. 'Machine Learning and the Internet of Things' can play a promising role in the Agricultural industry, and help to increase food production while respecting the environment. This book explains how these technologies can be applied, offering many case studies developed in the research world.
Доп.точки доступа:
Jain, Vishal, (1983-) \editor.\
Chatterjee, Jyotir Moy, \editor.\
Kumar, Abhishek, \editor.\
Rathore, Pramod Singh, (1988-) \editor.\
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