Электронный каталог

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/49180D55-AA15-4935-86A3-82CCF72C7E8B. - 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 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/97A93777-D902-4206-BF79-465256E2AB5F. - 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)