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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.
2.
Подробнее
S 34
Schmarzo, Bill.
The the Economics of Data, Analytics, and Digital Transformation : : The Theorems, Laws, and Empowerments to Guide Your Organization's Digital Transformation. / Bill. Schmarzo, Borne, Kirk. - Birmingham : : Packt Publishing, Limited,, 2020. - 1 online resource (253 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/E1647E56-5900-4D04-AD98-D25B2220AA9B. - ISBN 9781800569133 (electronic book). - ISBN 1800569130 (electronic book)
Параллельные издания: Print version: : Schmarzo, Bill The the Economics of Data, Analytics, and Digital Transformation : The Theorems, Laws, and Empowerments to Guide Your Organization's Digital Transformation. - Birmingham : Packt Publishing, Limited,c2020. - ISBN 9781800561410
Содержание:
Cover -- Copyright -- Packt Page -- Foreword -- Contributor -- Table of Content -- Preface -- Chapter 1: The CEO Mandate: Become Value-driven, Not Data-driven -- The Data- and Value-Driven Mindsets, Defined -- Understanding the Big Data Business Model Maturity Index Phases -- Navigating the Big Data Business Model Maturity Index -- Transitioning from Business Monitoring to Business Insights -- Transitioning from Business Insights to Business Optimization -- Transitioning from Business Optimization to Insights Monetization -- Transitioning from Insights Monetization to Digital Transformation
Testing the Big Data Business Model Maturity Index -- Summary -- Further Reading -- Homework -- Chapter 2: Value Engineering: The Secret Sauce for Data Science Success -- Step 1: Identify a Strategic Business Initiative -- Step 2: Identify Key Business Stakeholders -- Step 3: Brainstorm and Prioritize Decisions (Use Cases) -- Step 4: Identify Supporting Analytics -- Step 5: Identify Potential Data Sources and Instrumentation Strategy -- Step 6: Identify Supporting Architecture and Technologies -- Summary -- Homework -- Chapter 3: A Review of Basic Economic Concepts -- The Economic Value Curve
The Law of Supply and Demand -- The Economic Multiplier Effect -- Marginal Costs and Sunk Costs -- Scarcity -- Postponement Theory -- Efficiency -- Capital -- Price Elasticity -- The Economic Utility Function -- Summary -- Further Reading -- Homework -- Chapter 4: University of San Francisco Economic Value of Data Research Paper -- Introduction -- Creating the Collaborative Value Creation Framework -- Chipotle Use Case -- Summary -- Citations -- Chapter 5: The Economic Value of Data Theorems -- EvD Theorem #1: Data, By Itself, Provides Little Value
EvD Theorem #2: Predictions, Not Data, Drive Value -- EvD Theorem #3: Predictions Drive Value Through Use Cases -- EvD Theorem #4: The Data Economic Multiplier Effect is the Real Game-changer -- EvD Theorem #5: Predictions Enable ""Do More with Less -- The Economic Value of Data Calculation -- Summary -- Further Reading -- Homework -- Chapter 6: The Economics of Artificial Intelligence -- Orphaned Analytics -- Role of Analytic Modules -- Composable, Reusable, Continuously Learning Analytic Module Architecture -- A Quick Primer on Deep Learning, Reinforcement Learning, and Artificial Intelligence
Case Study #1: Google TensorFlow -- Case Study #2: Tesla Autonomous Vehicles -- The Autonomous Holy Grail of AI -- Summary -- Further Reading -- Homework -- Chapter 7: The Schmarzo Economic Digital Assest Valuation Theorem -- The Economies of Scale -- The Economies of Learning -- Digital Economics Effect #1: Marginal Costs Flatten -- Digital Economics Effect #2: Economic Value of Digital Assets Grows -- Digital Economics Effect #3: Economic Value of Digital Assets Accelerates -- Implementing the Schmarzo Economic Digital Asset Valuation Theorem -- Summary -- Further Reading -- Homework
~РУБ DDC 658.4038
Рубрики: Information technology--Management.
Big data.
Database design & theory.
Data capture & analysis.
Information visualization.
Information architecture.
Computers--Data Processing.
Computers--Data Modeling & Design.
Big data.
Information technology--Management.
Аннотация: A comprehensive guide for seasoned business leaders who struggle with where and how to exploit the economics of data and analytics to gain true value from data, accelerate company operations through AI, and guide their digital transformation.
Доп.точки доступа:
Borne, Kirk.
S 34
Schmarzo, Bill.
The the Economics of Data, Analytics, and Digital Transformation : : The Theorems, Laws, and Empowerments to Guide Your Organization's Digital Transformation. / Bill. Schmarzo, Borne, Kirk. - Birmingham : : Packt Publishing, Limited,, 2020. - 1 online resource (253 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/E1647E56-5900-4D04-AD98-D25B2220AA9B. - ISBN 9781800569133 (electronic book). - ISBN 1800569130 (electronic book)
Параллельные издания: Print version: : Schmarzo, Bill The the Economics of Data, Analytics, and Digital Transformation : The Theorems, Laws, and Empowerments to Guide Your Organization's Digital Transformation. - Birmingham : Packt Publishing, Limited,c2020. - ISBN 9781800561410
Содержание:
Cover -- Copyright -- Packt Page -- Foreword -- Contributor -- Table of Content -- Preface -- Chapter 1: The CEO Mandate: Become Value-driven, Not Data-driven -- The Data- and Value-Driven Mindsets, Defined -- Understanding the Big Data Business Model Maturity Index Phases -- Navigating the Big Data Business Model Maturity Index -- Transitioning from Business Monitoring to Business Insights -- Transitioning from Business Insights to Business Optimization -- Transitioning from Business Optimization to Insights Monetization -- Transitioning from Insights Monetization to Digital Transformation
Testing the Big Data Business Model Maturity Index -- Summary -- Further Reading -- Homework -- Chapter 2: Value Engineering: The Secret Sauce for Data Science Success -- Step 1: Identify a Strategic Business Initiative -- Step 2: Identify Key Business Stakeholders -- Step 3: Brainstorm and Prioritize Decisions (Use Cases) -- Step 4: Identify Supporting Analytics -- Step 5: Identify Potential Data Sources and Instrumentation Strategy -- Step 6: Identify Supporting Architecture and Technologies -- Summary -- Homework -- Chapter 3: A Review of Basic Economic Concepts -- The Economic Value Curve
The Law of Supply and Demand -- The Economic Multiplier Effect -- Marginal Costs and Sunk Costs -- Scarcity -- Postponement Theory -- Efficiency -- Capital -- Price Elasticity -- The Economic Utility Function -- Summary -- Further Reading -- Homework -- Chapter 4: University of San Francisco Economic Value of Data Research Paper -- Introduction -- Creating the Collaborative Value Creation Framework -- Chipotle Use Case -- Summary -- Citations -- Chapter 5: The Economic Value of Data Theorems -- EvD Theorem #1: Data, By Itself, Provides Little Value
EvD Theorem #2: Predictions, Not Data, Drive Value -- EvD Theorem #3: Predictions Drive Value Through Use Cases -- EvD Theorem #4: The Data Economic Multiplier Effect is the Real Game-changer -- EvD Theorem #5: Predictions Enable ""Do More with Less -- The Economic Value of Data Calculation -- Summary -- Further Reading -- Homework -- Chapter 6: The Economics of Artificial Intelligence -- Orphaned Analytics -- Role of Analytic Modules -- Composable, Reusable, Continuously Learning Analytic Module Architecture -- A Quick Primer on Deep Learning, Reinforcement Learning, and Artificial Intelligence
Case Study #1: Google TensorFlow -- Case Study #2: Tesla Autonomous Vehicles -- The Autonomous Holy Grail of AI -- Summary -- Further Reading -- Homework -- Chapter 7: The Schmarzo Economic Digital Assest Valuation Theorem -- The Economies of Scale -- The Economies of Learning -- Digital Economics Effect #1: Marginal Costs Flatten -- Digital Economics Effect #2: Economic Value of Digital Assets Grows -- Digital Economics Effect #3: Economic Value of Digital Assets Accelerates -- Implementing the Schmarzo Economic Digital Asset Valuation Theorem -- Summary -- Further Reading -- Homework
Рубрики: Information technology--Management.
Big data.
Database design & theory.
Data capture & analysis.
Information visualization.
Information architecture.
Computers--Data Processing.
Computers--Data Modeling & Design.
Big data.
Information technology--Management.
Аннотация: A comprehensive guide for seasoned business leaders who struggle with where and how to exploit the economics of data and analytics to gain true value from data, accelerate company operations through AI, and guide their digital transformation.
Доп.точки доступа:
Borne, Kirk.
Страница 1, Результатов: 2