Electronic catalog

DDC 006.3015
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

Farrell, Peter,. The statistics and calculus workshop [Электронный ресурс] : a comprehensive introduction to mathematics in Python for artificial intelligence applications / / Peter Farrell [and five others]., 2020. - 1 online resource с. (Введено оглавление)

DDC 005.13/3
S 95
Sutor, Robert S.
    Dancing with Python : : Learn Python Software Development from Scratch and Get Started with Quantum Computing. / Robert S. Sutor. - Birmingham : : Packt Publishing, Limited,, 2021. - 1 online resource (745 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/6B4FCB22-C9AF-4A0B-AD94-CF514B89927D. - ISBN 9781801071628 (electronic book). - ISBN 1801071624 (electronic book)
Параллельные издания: Print version: : Sutor, Robert S. Dancing with Python. - Birmingham : Packt Publishing, Limited,c2021

Sutor, Robert S. Dancing with Python : [Электронный ресурс] : Learn Python Software Development from Scratch and Get Started with Quantum Computing. / Robert S. Sutor, 2021. - 1 online resource (745 p.) с.

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
A 62
Antić, Zhenya.
    Python Natural Language Processing Cookbook [[electronic resource] :] : Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks. / Zhenya. Antić. - Birmingham : : Packt Publishing, Limited,, 2021. - 1 online resource (285 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/DE029E14-9D24-448A-92F0-7D33053C1CC2. - ISBN 1838987789. - ISBN 9781838987787 (electronic bk.)
Description based upon print version of record. How to do it...
Параллельные издания: Print version: : Antić, Zhenya Python Natural Language Processing Cookbook. - Birmingham : Packt Publishing, Limited,c2021. - ISBN 9781838987312
    Содержание:
Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Chapter 1: Learning NLP Basics -- Technical requirements -- Dividing text into sentences -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Dividing sentences into words -- tokenization -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Parts of speech tagging -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Word stemming -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also
Combining similar words -- lemmatization -- Getting ready -- How to do it... -- How it works... -- There's more... -- Removing stopwords -- Getting ready... -- How to do it... -- How it works... -- There's more... -- Chapter 2: Playing with Grammar -- Technical requirements -- Counting nouns -- plural and singular nouns -- Getting ready -- How to do it... -- How it works... -- There's more... -- Getting the dependency parse -- Getting ready -- How to do it... -- How it works... -- See also -- Splitting sentences into clauses -- Getting ready -- How to do it... -- How it works... -- Extracting noun chunks -- Getting ready
How to do it... -- How it works... -- There's more... -- See also -- Extracting entities and relations -- Getting ready -- How to do it... -- How it works... -- There's more... -- Extracting subjects and objects of the sentence -- Getting ready -- How to do it... -- How it works... -- There's more... -- Finding references -- anaphora resolution -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 3: Representing Text -- Capturing Semantics -- Technical requirements -- Putting documents into a bag of words -- Getting ready -- How to do it... -- How it works... -- There's more...
Constructing the N-gram model -- Getting ready -- How to do it... -- How it works... -- There's more... -- Representing texts with TF-IDF -- Getting ready -- How to do it... -- How it works... -- There's more... -- Using word embeddings -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Training your own embeddings model -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Representing phrases -- phrase2vec -- Getting ready -- How to do it... -- How it works... -- See also -- Using BERT instead of word embeddings -- Getting ready -- How to do it...
How it works... -- Getting started with semantic search -- Getting ready -- How to do it... -- How it works... -- See also -- Chapter 4: Classifying Texts -- Technical requirements -- Getting the dataset and evaluation baseline ready -- Getting ready -- How to do it... -- How it works... -- Performing rule-based text classification using keywords -- Getting ready -- How to do it... -- How it works... -- There's more... -- Clustering sentences using K-means -- unsupervised text classification -- Getting ready -- How to do it... -- How it works... -- Using SVMs for supervised text classification -- Getting ready

Antić, Zhenya. Python Natural Language Processing Cookbook [[electronic resource] :] : Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks. / Zhenya. Antić, 2021. - 1 online resource (285 p.) с. (Введено оглавление)

DDC 005.13/3
J 16
Jafari, Roy.
    Hands-On Data Preprocessing in Python : : Learn How to Effectively Prepare Data for Successful Data Analytics. / Roy. Jafari. - Birmingham : : Packt Publishing, Limited,, 2022. - 1 online resource (602 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/8523FC79-D74D-4BD1-A333-17AD10D886F8. - ISBN 9781801079952 (electronic book). - ISBN 1801079951 (electronic book)
Параллельные издания: Print version: : Jafari, Roy. Hands-On Data Preprocessing in Python. - Birmingham : Packt Publishing, Limited, ©2022

Jafari, Roy. Hands-On Data Preprocessing in Python : [Электронный ресурс] : Learn How to Effectively Prepare Data for Successful Data Analytics. / Roy. Jafari, 2022. - 1 online resource (602 pages) с.

DDC 005.13/3
B 92
BUELTA, JAIME.
    PYTHON ARCHITECTURE PATTERNS [[electronic resource] :] : master api design, event-driven structures, and package management... in python. / JAIME. BUELTA. - [Б. м.] : PACKT PUBLISHING LIMITED,, 2022. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/4A190D0A-CCD1-42D7-8DFE-9CA63DA3FEE1. - ISBN 9781801811774 (electronic bk.). - ISBN 1801811776 (electronic bk.)
Параллельные издания: Print version: :

BUELTA, JAIME. PYTHON ARCHITECTURE PATTERNS [[electronic resource] :] : master api design, event-driven structures, and package management... in python. / JAIME. BUELTA, 2022. - 1 online resource с.

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)