1. Objective The goal is to build a complete Deep Learning application, including: -Training a sentiment analysis model. -Saving the…
ARTIFICIAL INTELLIGENCE (23) – Deep learning (21) Text Representations in NLP (3) Word embeddings
Word embeddings are a more advanced way to represent words compared to count vectors. Instead of just counting words, each…
ARTIFICIAL INTELLIGENCE (22) – Deep learning (20) Text Representations in NLP (2) Count vectorizer
A Count Vectorizer is a way for a computer to understand a sentence by counting words instead of looking at…
ARTIFICIAL INTELLIGENCE (21) – Deep learning (19) Text Representations in NLP (1) One-hot encoding
We are going to explore the inputs representations in the process of transforming a text corpus into different input formats…
ARTIFICIAL INTELLIGENCE (20) – Deep learning (18) Embeding Lab
I think the best way to start talking about embeddings is to propose a Lab about it. In the next…
ARTIFICIAL INTELLIGENCE (19) – Deep learning (17) Byte Pair Encoding: A Powerful Subword Tokenization Method in Modern NLP
Processing human language remains one of the most intricate challenges in artificial intelligence. While humans communicate through words, nuance, and…
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