Natural Language Processing Fundamentals
Chapter 1: NLP Concepts and Advanced Text Processing
The Natural Language Processing Pipeline
Advanced Tokenization Methods
Stemming and Lemmatization Compared
Handling Noise in Text Data
Advanced Stop Word Customization
Text Normalization Techniques
Hands-on Practical: Building Preprocessing Pipelines
Quiz for Chapter 1
Chapter 2: Feature Engineering for Text Representation
From Bag-of-Words to TF-IDF
Calculating TF-IDF Scores
Using N-grams to Capture Context
Introduction to Feature Hashing
Dimensionality Reduction for Text Features
Comparing Different Text Representation Methods
Hands-on Practical: Generating Text Features
Quiz for Chapter 2
Chapter 3: Supervised Learning for Text Classification
Classification Algorithms Review
Applying Classifiers to Text Data
Model Evaluation Metrics for Classification
Cross-Validation Strategies
Hyperparameter Tuning for Text Models
Addressing Imbalanced Datasets
Practice: Building a Text Classifier
Quiz for Chapter 3
Chapter 4: Understanding Sequential Data with Embeddings
Limitations of Frequency-Based Models
Introduction to Distributional Semantics
Word Embedding Concepts
Word2Vec: CBOW and Skip-gram Architectures
GloVe: Global Vectors for Word Representation
Visualizing Word Embeddings
Using Pre-trained Word Embedding Models
Hands-on Practical: Working with Word Embeddings
Quiz for Chapter 4
Chapter 5: Introduction to Sequence Models for NLP
The Need for Sequence Awareness
Recurrent Neural Network (RNN) Basics
Understanding the Vanishing Gradient Problem
Long Short-Term Memory (LSTM) Networks
Gated Recurrent Units (GRUs)
Applying Sequence Models to Text
Hands-on Practical: Building a Simple Sequence Model
Quiz for Chapter 5
Applying Sequence Models to Text
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Provides a comprehensive introduction to deep learning, including detailed explanations of recurrent neural networks, LSTMs, and GRUs.
- Efficient Estimation of Word Representations in Vector Space, Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean, 2013 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1301.3781 - Introduces the Word2Vec models (CBOW and Skip-gram) for learning word embeddings, which are fundamental for preparing text for sequence models.
- Sequence to Sequence Learning with Neural Networks, Ilya Sutskever, Oriol Vinyals, Quoc V. Le, 2014 Advances in Neural Information Processing Systems (NIPS), Vol. 27 DOI: 10.48550/arXiv.1409.3215 - Presents the encoder-decoder architecture using LSTMs for mapping input sequences to output sequences of potentially different lengths, applicable to machine translation and other seq2seq tasks.
- CS224n: Natural Language Processing with Deep Learning, Diyi Yang, Tatsunori Hashimoto, 2023 (Stanford University) - Provides lecture notes, assignments, and videos covering sequence models, word embeddings, and various NLP applications discussed in the section.