Электронный каталог

DDC 005.133
M 70
Miller, Curtis.
    Training Systems Using Python Statistical Modeling [[electronic resource] :] : Explore Popular Techniques for Modeling Your Data in Python. / Curtis. Miller. - Birmingham : : Packt Publishing, Limited,, 2019. - 1 online resource (284 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/BEE0D235-53DA-4E09-BAC0-3272262E0143. - ISBN 1838820647. - ISBN 9781838820640 (electronic bk.)
Description based upon print version of record. The silhouette method
Параллельные издания: Print version: : Miller, Curtis Training Systems Using Python Statistical Modeling : Explore Popular Techniques for Modeling Your Data in Python. - Birmingham : Packt Publishing, Limited,c2019. - ISBN 9781838823733
    Содержание:
Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Classical Statistical Analysis; Technical requirements; Computing descriptive statistics; Preprocessing the data; Computing basic statistics; Classical inference for proportions; Computing confidence intervals for proportions; Hypothesis testing for proportions; Testing for common proportions; Classical inference for means; Computing confidence intervals for means; Hypothesis testing for means; Testing with two samples; One-way analysis of variance (ANOVA); Diving into Bayesian analysis
How Bayesian analysis worksUsing Bayesian analysis to solve a hit-and-run; Bayesian analysis for proportions; Conjugate priors for proportions; Credible intervals for proportions; Bayesian hypothesis testing for proportions; Comparing two proportions; Bayesian analysis for means; Credible intervals for means; Bayesian hypothesis testing for means; Testing with two samples; Finding correlations; Testing for correlation; Summary; Chapter 2: Introduction to Supervised Learning; Principles of machine learning; Checking the variables using the iris dataset; The goal of supervised learning
Training modelsIssues in training supervised learning models; Splitting data; Cross-validation; Evaluating models; Accuracy; Precision; Recall; F1 score; Classification report; Bayes factor; Summary; Chapter 3: Binary Prediction Models; K-nearest neighbors classifier; Training a kNN classifier; Hyperparameters in kNN classifiers; Decision trees; Fitting the decision tree; Visualizing the tree; Restricting tree depth; Random forests; Optimizing hyperparameters; Naive Bayes classifier; Preprocessing the data; Training the classifier; Support vector machines; Training a SVM; Logistic regression
Fitting a logit modelExtending beyond binary classifiers; Multiple outcomes for decision trees; Multiple outcomes for random forests; Multiple outcomes for Naive Bayes; One-versus-all and one-versus-one classification; Summary; Chapter 4: Regression Analysis and How to Use It; Linear models; Fitting a linear model with OLS; Performing cross-validation; Evaluating linear models; Using AIC to pick models; Bayesian linear models; Choosing a polynomial; Performing Bayesian regression; Ridge regression; Finding the right alpha value; LASSO regression; Spline interpolation
Using SciPy for interpolation2D interpolation; Summary; Chapter 5: Neural Networks; An introduction to perceptrons; Neural networks; The structure of a neural network; Types of neural networks; The MLP model; MLPs for classification; Optimization techniques; Training the network; Fitting an MLP to the iris dataset; Fitting an MLP to the digits dataset; MLP for regression; Summary; Chapter 6: Clustering Techniques; Introduction to clustering; Computing distances; Exploring the k-means algorithm; Clustering the iris dataset; Compressing images with k-means; Evaluating clusters; The elbow method

Miller, Curtis. Training Systems Using Python Statistical Modeling [[electronic resource] :] : Explore Popular Techniques for Modeling Your Data in Python. / Curtis. Miller, 2019. - 1 online resource (284 p.) с. (Введено оглавление)

DDC 005.1
J 44
Jesús, Sofía De.
    Applied Computational Thinking with Python [[electronic resource] :] : Design Algorithmic Solutions for Complex and Challenging Real-World Problems. / Sofía De. Jesús, Martinez, Dayrene. - Birmingham : : Packt Publishing, Limited,, 2020. - 1 online resource (420 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/4EFD77A3-7336-49FD-8452-766E1986988A. - ISBN 9781839216763. - ISBN 183921676X
Description based upon print version of record.
Параллельные издания: Print version: : Jesús, Sofía De Applied Computational Thinking with Python : Design Algorithmic Solutions for Complex and Challenging Real-World Problems. - Birmingham : Packt Publishing, Limited,c2020. - ISBN 9781839219436
    Содержание:
Cover -- Title Page -- Copyright and Credits -- Dedicated -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Introduction to Computational Thinking -- Chapter 1: Fundamentals of Computer Science -- Technical requirements -- Introduction to computer science -- Learning about computers and the binary system -- Understanding theoretical computer science -- Algorithms -- Coding theory -- Computational biology -- Data structures -- Information theory -- Automata theory -- Formal language theory -- Symbolic computation -- Computational geometry
Computational number theory -- Learning about a system's software -- Operating systems -- Application software -- Understanding computing -- Architecture -- Programming languages -- Learning about data types and structures -- Data types -- Data structures -- Summary -- Chapter 2: Elements of Computational Thinking -- Technical requirements -- Understanding computational thinking -- Problem 1 -- Conditions -- Decomposing problems -- Recognizing patterns -- Problem 2 -- Mathematical algorithms and generalization -- Generalizing patterns -- Designing algorithms -- Additional problems
Problem 2 -- Children's soccer party -- Problem 3 -- Savings and interest -- Summary -- Chapter 3: Understanding Algorithms and Algorithmic Thinking -- Technical requirements -- Defining algorithms in depth -- Algorithms should be clear and unambiguous -- Algorithms should have inputs and outputs that are well defined -- Algorithms should have finiteness -- Algorithms have to be feasible -- Algorithms are language-independent -- Designing algorithms -- Problem 1 -- An office lunch -- Problem 2 -- A catering company -- Analyzing algorithms -- Algorithm analysis 1 -- States and capitals
Algorithm analysis 2 -- Terminating or not terminating? -- Summary -- Chapter 4: Understanding Logical Reasoning -- Technical requirements -- Understanding the importance of logical reasoning -- Applying inductive reasoning -- Applying deductive reasoning -- Using Boolean logic and operators -- The and operator -- The or operator -- The not operator -- Identifying logic errors -- Summary -- Chapter 5: Exploring Problem Analysis -- Technical requirements -- Understanding the problem definitions -- Problem 5A -- Building an online store -- Learning to decompose problems
Converting the flowchart into an algorithm -- Analyzing problems -- Problem 5B -- Analyzing a simple game problem -- Summary -- Chapter 6: Designing Solutions and Solution Processes -- Technical requirements -- Designing solutions -- Problem 1 -- A marketing survey -- Diagramming solutions -- Creating solutions -- Problem 2 -- Pizza order -- Problem 3 -- Delays and Python -- Summary -- Chapter 7: Identifying Challenges within Solutions -- Technical requirements -- Identifying errors in algorithm design -- Syntax errors -- Errors in logic -- Debugging algorithms -- Comparing solutions
Problem 1 -- Printing even numbers

Jesús, Sofía De. Applied Computational Thinking with Python [[electronic resource] :] : Design Algorithmic Solutions for Complex and Challenging Real-World Problems. / Sofía De. Jesús, Martinez, Dayrene., 2020. - 1 online resource (420 p.) с. (Введено оглавление)

DDC 006.35
K 22
Kasliwal, Nirant.
    Natural Language Processing with Python Quick Start Guide : : Going from a Python Developer to an Effective Natural Language Processing Engineer. / Nirant. Kasliwal. - Birmingham : : Packt Publishing Ltd,, 2018. - 1 online resource (177 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/2A2D6B6C-BFA4-433B-A308-9EE3B8394BA4. - ISBN 1788994108. - ISBN 9781788994101 (electronic bk.)
Weighted classifiers. Print version record.
Параллельные издания: Print version: : Kasliwal, Nirant. Natural Language Processing with Python Quick Start Guide : Going from a Python Developer to an Effective Natural Language Processing Engineer. - Birmingham : Packt Publishing Ltd, ©2018. - ISBN 9781789130386
    Содержание:
Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Text Classification; What is NLP?; Why learn about NLP?; You have a problem in mind; Technical achievement; Do something new; Is this book for you?; NLP workflow template; Understanding the problem; Understanding and preparing the data; Quick wins -- proof of concept; Iterating and improving; Algorithms; Pre-processing; Evaluation and deployment; Evaluation; Deployment; Example -- text classification workflow; Launchpad -- programming environment setup
Text classification in 30 lines of codeGetting the data; Text to numbers; Machine learning; Summary; Chapter 2: Tidying your Text; Bread and butter -- most common tasks; Loading the data; Exploring the loaded data; Tokenization; Intuitive -- split by whitespace; The hack -- splitting by word extraction; Introducing Regexes; spaCy for tokenization; How does the spaCy tokenizer work?; Sentence tokenization; Stop words removal and case change; Stemming and lemmatization; spaCy for lemmatization; -PRON-; Case-insensitive; Conversion -- meeting to meet; spaCy compared with NLTK and CoreNLP
Correcting spellingFuzzyWuzzy; Jellyfish; Phonetic word similarity; What is a phonetic encoding?; Runtime complexity; Cleaning a corpus with FlashText; Summary; Chapter 3: Leveraging Linguistics; Linguistics and NLP; Getting started; Introducing textacy; Redacting names with named entity recognition; Entity types; Automatic question generation; Part-of-speech tagging; Creating a ruleset; Question and answer generation using dependency parsing; Visualizing the relationship; Introducing textacy; Leveling up -- question and answer; Putting it together and the end; Summary
Chapter 4: Text Representations -- Words to NumbersVectorizing a specific dataset; Word representations; How do we use pre-trained embeddings?; KeyedVectors API; What is missing in both word2vec and GloVe?; How do we handle Out Of Vocabulary words?; Getting the dataset; Training fastText embedddings; Training word2vec embeddings; fastText versus word2vec; Document embedding; Understanding the doc2vec API; Negative sampling; Hierarchical softmax; Data exploration and model evaluation; Summary; Chapter 5: Modern Methods for Classification; Machine learning for text
Sentiment analysis as text classification Simple classifiers; Optimizing simple classifiers; Ensemble methods; Getting the data; Reading data; Simple classifiers; Logistic regression; Removing stop words; Increasing ngram range; Multinomial Naive Bayes; Adding TF-IDF; Removing stop words; Changing fit prior to false; Support vector machines; Decision trees; Random forest classifier; Extra trees classifier; Optimizing our classifiers; Parameter tuning using RandomizedSearch; GridSearch; Ensembling models; Voting ensembles -- Simple majority (aka hard voting); Voting ensembles -- soft voting

Kasliwal, Nirant. Natural Language Processing with Python Quick Start Guide : [Электронный ресурс] : Going from a Python Developer to an Effective Natural Language Processing Engineer. / Nirant. Kasliwal, 2018. - 1 online resource (177 pages) с. (Введено оглавление)