Hands-On Python Natural Language Processing
Aman Kedia Mayank Rasu更新时间：2021-06-18 18:29:09
最新章节：Leave a review - let other readers know what you think封面
版权信息
About Packt
Why subscribe?
About the authors
Preface
Section 1: Introduction
Understanding the Basics of NLP
Programming languages versus natural languages
Why should I learn NLP?
Current applications of NLP
Summary
NLP Using Python
Technical requirements
Understanding Python with NLP
Important Python libraries
Web scraping libraries and methodology
Overview of Jupyter Notebook
Summary
Section 2: Natural Language Representation and Mathematics
Building Your NLP Vocabulary
Technical requirements
Lexicons
Phonemes graphemes and morphemes
Tokenization
Understanding word normalization
Summary
Transforming Text into Data Structures
Technical requirements
Understanding vectors and matrices
Exploring the Bag-of-Words architecture
TF-IDF vectors
Distance/similarity calculation between document vectors
One-hot vectorization
Building a basic chatbot
Summary
Word Embeddings and Distance Measurements for Text
Technical requirements
Understanding word embeddings
Demystifying Word2vec
Training a Word2vec model
Word mover’s distance
Summary
Exploring Sentence- Document- and Character-Level Embeddings
Technical requirements
Venturing into Doc2Vec
Exploring fastText
Understanding Sent2Vec and the Universal Sentence Encoder
Summary
Section 3: NLP and Learning
Identifying Patterns in Text Using Machine Learning
Technical requirements
Introduction to ML
Data preprocessing
The Naive Bayes algorithm
The SVM algorithm
Productionizing a trained sentiment analyzer
Summary
From Human Neurons to Artificial Neurons for Understanding Text
Technical requirements
Exploring the biology behind neural networks
How does a neural network learn?
Understanding regularization
Let's talk Keras
Building a question classifier using neural networks
Summary
Applying Convolutions to Text
Technical requirements
What is a CNN?
Detecting sarcasm in text using CNNs
Summary
Capturing Temporal Relationships in Text
Technical requirements
Baby steps toward understanding RNNs
Vanishing and exploding gradients
Architectural forms of RNNs
Giving memory to our networks – LSTMs
Building a text generator using LSTMs
Exploring memory-based variants of the RNN architecture
Summary
State of the Art in NLP
Technical requirements
Seq2Seq modeling
Translating between languages using Seq2Seq modeling
Let's pay some attention
Transformers
BERT
Summary
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更新时间：2021-06-18 18:29:09