Learning AI is really easy Ⅲ- Learn natural language processing easily-this is how computers understand language
Chapter 1 Introduction to Natural Language Processing
1-1 Introduction
1-2 Introduction to Natural Language Processing
1-3 Traditional Natural Language Processing
1-4 Natural Language Processing with Deep Learning
1-5 Summary
Chapter 2 The first step in learning Chinese with a computer: word segmentation
2-1 Introduction
2-2 Corpus
2-3 Maximum matching method
2-4 Word Segmentation Tool
2-5 Chinese word segmentation tool - Jieba
2-6 Academia Sinica's word segmentation system - CKipTagger
2-7 Summary
Chapter 3 Statistical Natural Language Processing
3-1 Introduction
3-2 One-hot representation
3-3 Bag of Words Representation
3-4 TF-IDF representation
3-5 N-grams
3-6 Summary
Chapter 4: Neural-based Natural Language Processing
4-1 Introduction
4-2 Word Embedding
4-3 King – He + She = Queen
4-4 Skip-gram model and CBOW model
4-5 Training vocabulary vectors
4-6 Summary
Chapter 5 Introduction to Recurrent Neural Networks
5-1 Introduction
5-2 Understanding Sequence Data
5-3 Evolution of Neural Networks
5-4 Recurrent Neural Networks
5-5 Types of Recurrent Neural Networks
5-6 Long Short-Term Memory Neural Network
5-7 Summary
Chapter 6 RNN and LSTM Implementation: IMDB Sentiment Analysis
6-1 Introduction
6-2 Loading and understanding the IMDB online movie database
6-3 Data Preprocessing
6-4 Constructing RNN and LSTM models
6-5 Summary
Chapter 7 RNN and LSTM Implementation: Part-of-Speech Tagging
7-1 Introduction
7-2 Loading and understanding part-of-speech tagging datasets
7-3 Data Preprocessing
7-4 Building a dictionary
7-5 Obtaining training and test sets
7-6 TensorFlow data pipeline
7-7 Constructing RNN and LSTM models
7-8 Summary
Appendix A Python Basic Syntax Query Table
A-1 Basic Python concepts
Appendix B Introduction to using Google Colab
B-1 Introduction to Google Colaboratory
B-2 Notebook Configuration Settings
B-3 View the current Python version and package list