Tokenization in NLP: From Basics to Advanced Techniques
Tokenization stands at the forefront of teaching machines to interpret human language. It's the critical step that allows algorithms to navigate the intricacies of our communication.
In our live talk, Suman Debnath, Principal Developer Advocate for Machine Learning at Amazon Web Services, will dive deep into this foundational element, sharing how it enables machines to decode and process human speech through the lens of natural language processing (NLP).
Explore vital processes that bridge human communication with artificial intelligence, enhancing your understanding of NLP's foundational techniques and their implications for the future of technology.
Key Takeaways:
🔹 Understand tokenization's impact on language models
🔹 Learn text splitting for deeper analysis
🔹 Explore Byte Pair Encoding's efficiency
🔹 Discover sliding windows for better training data
🔹 Learn about converting tokens into vectors
#NLP #Tokenization #LanguageModel #BytePair #TextAnalysis #DataScience #MachineLearning #AI ##Vectorization #ArtificialIntelligence #dataprocessing
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Table of Contents:
00:00 Introduction
04:30 Understanding Word Embeddings
06:30 Tokenizing Text
11:03 Converting Tokens into Token IDs
17:05 Adding Special Context Tokens
25:25 BytePair Encoding
39:20 Data Sampling with a Sliding Window
47:50 Creating Token Embeddings
50:46 Encoding Word Positions
55:55 Positional Encoding
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Resources:
https://github.com/debnsuma/nlp-embed...
https://github.com/build-on-aws/llm-r...
https://iitm-pod.slides.com/arunpraka...
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