Электронный каталог

DDC 006.31
K 91
Kumar, Rahul.
    Machine Learning Quick Reference [[electronic resource] :] : Quick and Essential Machine Learning Hacks for Training Smart Data Models. / Rahul. Kumar. - Birmingham : : Packt Publishing Ltd,, 2019. - 1 online resource (283 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/BADA2D47-8528-4914-850C-28F772467153. - ISBN 9781788831611 (electronic bk.). - ISBN 1788831616 (electronic bk.)
Description based upon print version of record. Importing the library
Параллельные издания: Print version: : Kumar, Rahul Machine Learning Quick Reference : Quick and Essential Machine Learning Hacks for Training Smart Data Models. - Birmingham : Packt Publishing Ltd,c2019. - ISBN 9781788830577
    Содержание:
Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Quantifying Learning Algorithms; Statistical models; Learning curve; Machine learning; Wright's model; Curve fitting; Residual; Statistical modeling - the two cultures of Leo Breiman; Training data development data -- test data; Size of the training, development, and test set; Bias-variance trade off; Regularization; Ridge regression (L2); Least absolute shrinkage and selection operator ; Cross-validation and model selection; K-fold cross-validation
Model selection using cross-validation0.632 rule in bootstrapping; Model evaluation; Confusion matrix; Receiver operating characteristic curve; Area under ROC; H-measure; Dimensionality reduction; Summary; Chapter 2: Evaluating Kernel Learning; Introduction to vectors; Magnitude of the vector; Dot product; Linear separability; Hyperplanes ; SVM; Support vector; Kernel trick; Kernel; Back to Kernel trick; Kernel types; Linear kernel; Polynomial kernel; Gaussian kernel; SVM example and parameter optimization through grid search; Summary; Chapter 3: Performance in Ensemble Learning
What is ensemble learning?Ensemble methods ; Bootstrapping; Bagging; Decision tree; Tree splitting; Parameters of tree splitting; Random forest algorithm; Case study; Boosting; Gradient boosting; Parameters of gradient boosting; Summary; Chapter 4: Training Neural Networks; Neural networks; How a neural network works; Model initialization; Loss function; Optimization; Computation in neural networks; Calculation of activation for H1; Backward propagation; Activation function; Types of activation functions; Network initialization; Backpropagation; Overfitting; Prevention of overfitting in NNs
Vanishing gradient Overcoming vanishing gradient; Recurrent neural networks; Limitations of RNNs; Use case; Summary; Chapter 5: Time Series Analysis; Introduction to time series analysis; White noise; Detection of white noise in a series; Random walk; Autoregression; Autocorrelation; Stationarity; Detection of stationarity; AR model; Moving average model; Autoregressive integrated moving average; Optimization of parameters; AR model; ARIMA model; Anomaly detection; Summary; Chapter 6: Natural Language Processing; Text corpus; Sentences; Words; Bags of words; TF-IDF
Executing the count vectorizerExecuting TF-IDF in Python; Sentiment analysis; Sentiment classification; TF-IDF feature extraction; Count vectorizer bag of words feature extraction; Model building count vectorization; Topic modeling ; LDA architecture; Evaluating the model; Visualizing the LDA; The Naive Bayes technique in text classification; The Bayes theorem; How the Naive Bayes classifier works; Summary; Chapter 7: Temporal and Sequential Pattern Discovery; Association rules; Apriori algorithm; Finding association rules; Frequent pattern growth; Frequent pattern tree growth; Validation

Kumar, Rahul. Machine Learning Quick Reference [[electronic resource] :] : Quick and Essential Machine Learning Hacks for Training Smart Data Models. / Rahul. Kumar, 2019. - 1 online resource (283 p.) с. (Введено оглавление)

DDC 005.3
H 65
High, Rob,.
    Cognitive Computing with IBM Watson [[electronic resource] /] / Rob High. - 1st edition. - 1788478298. - [Б. м.] : Packt Publishing,, 2019. - 1 online resource (256 pages) ( час. мин.), 1788478298. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/5D176F61-54C5-48F7-88F9-12FB37F41ED5. - ISBN 9781788478984 (electronic bk.). - ISBN 1788478983 (electronic bk.)
Online resource; Title from title page (viewed April 30, 2019).

High, Rob,. Cognitive Computing with IBM Watson [[electronic resource] /] / Rob High., 2019. - 1 online resource (256 pages) с.

DDC 006.312
S 17
Salcedo, Jesus.
    Machine Learning for Data Mining [[electronic resource] :] : Improve Your Data Mining Capabilities with Advanced Predictive Modeling. / Jesus. Salcedo. - Birmingham : : Packt Publishing, Limited,, 2019. - 1 online resource (247 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/DB31EBDC-325D-4F42-81DF-F1B237EA3081. - ISBN 1838821554 (electronic bk.). - ISBN 9781838821555 (electronic bk.)
Description based upon print version of record.
Параллельные издания: Print version: : Salcedo, Jesus Machine Learning for Data Mining : Improve Your Data Mining Capabilities with Advanced Predictive Modeling. - Birmingham : Packt Publishing, Limited,c2019. - ISBN 9781838828974

Salcedo, Jesus. Machine Learning for Data Mining [[electronic resource] :] : Improve Your Data Mining Capabilities with Advanced Predictive Modeling. / Jesus. Salcedo, 2019. - 1 online resource (247 p.) с.

DDC 006.31
M 55
Mengle, Saket S. R.,
    Mastering machine learning on AWS : : advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow / / Saket S.R. Mengle, Maximo Gurmendez. - Birmingham, UK : : Packt Publishing, Limited,, 2019. - 1 online resource (293 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/E64DB41C-5FE0-49C0-858F-11551992A3ED. - ISBN 1789347505 (ebook). - ISBN 9781789347500 (electronic bk.)
Description based on print version record.
Параллельные издания: Print version: : Mengle, Saket S. R. Mastering machine learning on AWS : advanced machine learning in Python Using SageMaker, Apache Spark, and TensorFlow. - Birmingham : Packt Publishing, Limited, ©2019. - ISBN 9781789349795
    Содержание:
Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: Machine Learning on AWS; Chapter 1: Getting Started with Machine Learning for AWS; How AWS empowers data scientists; Using AWS tools for machine learning; Identifying candidate problems that can be solved using machine learning; Machine learning project life cycle; Data gathering; Evaluation metrics; Algorithm selection; Deploying models; Summary; Exercise; Section 2: Implementing Machine Learning Algorithms at Scale on AWS
Chapter 2: Classifying Twitter Feeds with Naive BayesClassification algorithms; Feature types; Nominal features; Ordinal features; Continuous features; Naive Bayes classifier; Bayes' theorem; Posterior; Likelihood; Prior probability; Evidence; How the Naive Bayes algorithm works; Classifying text with language models; Collecting the tweets; Preparing the data; Building a Naive Bayes model through SageMaker notebooks; Naïve Bayes model on SageMaker notebooks using Apache Spark; Using SageMaker's BlazingText built-in ML service; Naive Bayes - pros and cons; Summary; Exercises
Chapter 3: Predicting House Value with Regression AlgorithmsPredicting the price of houses; Understanding linear regression; Linear least squares estimation; Maximum likelihood estimation; Gradient descent; Evaluating regression models; Mean absolute error; Mean squared error; Root mean squared error; R-squared; Implementing linear regression through scikit-learn; Implementing linear regression through Apache Spark; Implementing linear regression through SageMaker's linear Learner; Understanding logistic regression; Logistic regression in Spark; Pros and cons of linear models; Summary
Chapter 4: Predicting User Behavior with Tree-Based MethodsUnderstanding decision trees; Recursive splitting; Types of decision trees; Cost functions; Gini Impurity; Information gain; Criteria to stop splitting trees; Understanding random forest algorithms; Understanding gradient boosting algorithms; Predicting clicks on log streams; Introduction to Elastic Map Reduce (EMR); Training with Apache Spark on EMR; Getting the data; Preparing the data; Categorical encoding; One-hot encoding; Training a model; Evaluating our model; Area Under ROC Curve; Area under the precision-recall curve; Training tree ensembles on EMR Training gradient-boosted trees with the SageMaker services; Preparing the data; Training with SageMaker XGBoost; Applying and evaluating the model; Summary; Exercises
Chapter 5: Customer Segmentation Using Clustering Algorithms; Understanding How Clustering Algorithms Work; k-means clustering; Euclidean distance; Manhattan distance; Hierarchical clustering; Agglomerative clustering; Divisive clustering; Clustering with Apache Spark on EMR; Clustering with Spark and SageMaker on EMR; Understanding the purpose of the IAM role; Summary; Exercises; Chapter 6: Analyzing Visitor Patterns to Make Recommendations

Mengle, Saket S. R., Mastering machine learning on AWS : [Электронный ресурс] : advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow / / Saket S.R. Mengle, Maximo Gurmendez., 2019. - 1 online resource (293 pages) с. (Введено оглавление)

DDC 332.0285631
K 51
Klaas, Jannes,.
    Machine learning for finance : : the practical guide to using data-driven algorithms in banking, insurance, and investments / / Jannes Klaas. - Birmingham : : Packt Publishing, Limited,, 2019. - 1 online resource (457 pages). - (Expert insight). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/D8285CEE-5285-4F93-9C81-31FBA9712F32. - ISBN 1789134692. - ISBN 9781789134698 (electronic bk.)
Print version record.
Параллельные издания: Print version: : Klaas, Jannes. Machine Learning for Finance : The Practical Guide to Using Data-Driven Algorithms in Banking, Insurance, and Investments. - Birmingham : Packt Publishing, Limited, ©2019. - ISBN 9781789136364
    Содержание:
Cover; Copyright; Mapt upsell; Contributors; Table of Contents; Preface; Chapter 1: Neural Networks and Gradient-Based Optimization; Our journey in this book; What is machine learning?; Supervised learning; Unsupervised learning; Reinforcement learning; The unreasonable effectiveness of data; All models are wrong; Setting up your workspace; Using Kaggle kernels; Running notebooks locally; Installing TensorFlow; Installing Keras; Using data locally; Using the AWS deep learning AMI; Approximating functions; A forward pass; A logistic regressor; Python version of our logistic regressor
Optimizing model parametersMeasuring model loss; Gradient descent; Backpropagation; Parameter updates; Putting it all together; A deeper network; A brief introduction to Keras; Importing Keras; A two-layer model in Keras; Stacking layers; Compiling the model; Training the model; Keras and TensorFlow; Tensors and the computational graph; Exercises; Summary; Chapter 2: Applying Machine Learning to Structured Data; The data; Heuristic, feature-based, and E2E models; The machine learning software stack; The heuristic approach; Making predictions using the heuristic model; The F1 score
Evaluating with a confusion matrixThe feature engineering approach; A feature from intuition -- fraudsters don't sleep; Expert insight -- transfer, then cash out; Statistical quirks -- errors in balances; Preparing the data for the Keras library; One-hot encoding; Entity embeddings; Tokenizing categories; Creating input models; Training the model; Creating predictive models with Keras; Extracting the target; Creating a test set; Creating a validation set; Oversampling the training data; Building the model; Creating a simple baseline; Building more complex models
A brief primer on tree-based methodsA simple decision tree; A random forest; XGBoost; E2E modeling; Exercises; Summary; Chapter 3: Utilizing Computer Vision; Convolutional Neural Networks; Filters on MNIST; Adding a second filter; Filters on color images; The building blocks of ConvNets in Keras; Conv2D; Kernel size; Stride size; Padding; Input shape; Simplified Conv2D notation; ReLU activation; MaxPooling2D; Flatten; Dense; Training MNIST; The model; Loading the data; Compiling and training; More bells and whistles for our neural network; Momentum; The Adam optimizer; Regularization
L2 regularizationL1 regularization; Regularization in Keras; Dropout; Batchnorm; Working with big image datasets; Working with pretrained models; Modifying VGG-16; Random image augmentation; Augmentation with ImageDataGenerator; The modularity tradeoff; Computer vision beyond classification; Facial recognition; Bounding box prediction; Exercises; Summary; Chapter 4: Understanding Time Series; Visualization and preparation in pandas; Aggregate global feature statistics; Examining the sample time series; Different kinds of stationarity; Why stationarity matters; Making a time series stationary; When to ignore stationarity issues.

Klaas, Jannes,. Machine learning for finance : [Электронный ресурс] : the practical guide to using data-driven algorithms in banking, insurance, and investments / / Jannes Klaas., 2019. - 1 online resource (457 pages) с. (Введено оглавление)

DDC 620.00285/63
B 40
Bekdaş, Gebrail.
    Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering / Gebrail. Bekdaş, Nigdeli, Sinan Melih., Yücel, Melda. - Hershey : : IGI Global,, 2019. - 1 online resource (327 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/D40641E1-9BF1-4C36-A4FC-3BA75D00E247. - ISBN 179980304X. - ISBN 9781799803041 (electronic bk.)
Print version record.
Параллельные издания: Print version: : Bekdaş, Gebrail. Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering. - Hershey : IGI Global, ©2019. - ISBN 9781799803010
    Содержание:
Title Page; Copyright Page; Book Series; Table of Contents; Detailed Table of Contents; Preface; Chapter 1: Review and Applications of Machine Learning and Artificial Intelligence in Engineering; Chapter 2: Artificial Neural Networks (ANNs) and Solution of Civil Engineering Problems; Chapter 3: A Novel Prediction Perspective to the Bending Over Sheave Fatigue Lifetime of Steel Wire Ropes by Means of Artificial Neural Networks; Chapter 4: Introduction and Application Aspects of Machine Learning for Model Reference Adaptive Control With Polynomial Neurons
Chapter 5: Optimum Design of Carbon Fiber-Reinforced Polymer (CFRP) Beams for Shear Capacity via Machine Learning MethodsChapter 6: A Scientometric Analysis and a Review on Current Literature of Computer Vision Applications; Chapter 7: High Performance Concrete (HPC) Compressive Strength Prediction With Advanced Machine Learning Methods; Chapter 8: Artificial Intelligence Towards Water Conservation; Chapter 9: Analysis of Ground Water Quality Using Statistical Techniques; Chapter 10: Probe People and Vehicle-Based Data Sources Application in Smart Transportation
Chapter 11: Application of Machine Learning Methods for Passenger Demand Prediction in Transfer Stations of Istanbul's Public Transportation SystemChapter 12: Metaheuristics Approaches to Solve the Employee Bus Routing Problem With Clustering-Based Bus Stop Selection; Chapter 13: An Assessment of Imbalanced Control Chart Pattern Recognition by Artificial Neural Networks; Chapter 14: An Exploration of Machine Learning Methods for Biometric Identification Based on Keystroke Dynamics; Compilation of References; About the Contributors; Index

Bekdaş, Gebrail. Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering [Электронный ресурс] / Gebrail. Bekdaş, Nigdeli, Sinan Melih., Yücel, Melda., 2019. - 1 online resource (327 pages) с. (Введено оглавление)

DDC 794.81631
L 24
Lanham, Micheal,.
    Hands-on reinforcement learning for games : : implementing self-learning agents in games using artificial intelligence techniques / / Micheal Lanham. - Birmingham, UK : : Packt Publishing,, 2020. - 1 online resource (1 volume) : : il. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/794FA818-3BC4-4505-8A70-925376C4C725. - ISBN 9781839216770. - ISBN 1839216778
Description based on online resource; title from title page (Safari, viewed June 17, 2020).
Параллельные издания: Print version: : Lanham, Micheal. Hands-On Reinforcement Learning for Games : Implementing Self-Learning Agents in Games Using Artificial Intelligence Techniques. - Birmingham : Packt Publishing, Limited, ©2020. - ISBN 9781839214936

Lanham, Micheal,. Hands-on reinforcement learning for games : [Электронный ресурс] : implementing self-learning agents in games using artificial intelligence techniques / / Micheal Lanham., 2020. - 1 online resource (1 volume) : с.

DDC 006.3/843
A 67

    Applications of advanced machine intelligence in computer vision and object recognition : : emerging research and opportunities / / [edited by] Shouvik Chakraborty, Kalyani Mali. - Hershey, PA : : IGI Global, Engineering Science Reference,, [2020]. - 1 online resource (xx, 271 pages). : il. - (Advances in computational intelligence and robotics (ACIR) book series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/0F16A6B5-7095-4E35-8FE6-CDAE14A3BE3D. - ISBN 9781799827382 (electronic book). - ISBN 1799827380 (electronic book). - ISBN 9781799827399 (electronic bk.). - ISBN 1799827399 (electronic bk.)
Description based on online resource; title from digital title page (viewed on April 10, 2020).
Параллельные издания: Print version: : Applications of advanced machine intelligence in computer vision and object recognition. - Hershey, PA : Engineering Science Reference, 2020. - ISBN 9781799827368
    Содержание:
Chapter 1. A robust image encryption method using chaotic skew-tent map -- Chapter 2. Anisotropic diffusion-based color texture analysis for industrial application -- Chapter 3. A brief overview on intelligent computing-based biological data and image analysis -- Chapter 4. An advanced approach to detect edges of digital images for image segmentation -- Chapter 5. Fusion approach-based horticulture plant diseases identification using image processing -- Chapter 6. A generalized overview of the biomedical image processing from the big data perspective -- Chapter 7. Image fusion techniques for different multimodality medical images based on various conventional and hybrid algorithms for disease analysis -- Chapter 8. An overview of biomedical image analysis from the deep learning perspective -- Chapter 9. Segmentation-free word spotting in handwritten documents using scale space co-hog feature descriptors.

Applications of advanced machine intelligence in computer vision and object recognition : [Электронный ресурс] : emerging research and opportunities / / [edited by] Shouvik Chakraborty, Kalyani Mali., [2020]. - 1 online resource (xx, 271 pages). с. (Введено оглавление)

DDC 006.37
K 21
Kar, Krishnendu,.
    Advanced computer vision with TensorFlow 2.x : build advanced computer vision applications using machine learning and deep learning techniques / / Krishnendu Kar. - Birmingham : : Packt Publishing,, 2020. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/669C26B6-FFE3-4EF1-A518-11052A787903. - ISBN 9781838826932 (e-book). - ISBN 1838826939
Параллельные издания: Print version : :

Kar, Krishnendu,. Advanced computer vision with TensorFlow 2.x [Электронный ресурс] : build advanced computer vision applications using machine learning and deep learning techniques / / Krishnendu Kar., 2020. - 1 online resource с.

DDC 006.3/1
Q 23

    Quantum machine learning / edited by Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Sourav De, Elizabeth Behrman, Susanta Chakraborti. - 1515/9783110670707. - Berlin ; Boston : De Gruyter, ©2020. - 1 online resource : il ( час. мин.), 1515/9783110670707. - (De Gruyter Frontiers in Computational Intelligence ; volume 6). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/3F9AE1C8-92D4-4D47-8B9B-3F82CBC46664. - ISBN 9783110670721. - ISBN 3110670720. - ISBN 3110670704 (electronic book). - ISBN 9783110670707 (electronic bk.)
Description based on online resource; title from PDF title page (viewed on September 10, 2020)
Параллельные издания:
1. Print version :
2. Print version :

Quantum machine learning [Электронный ресурс] / edited by Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Sourav De, Elizabeth Behrman, Susanta Chakraborti, ©2020. - 1 online resource с.