Word embedding is a method used to map words of a vocabulary to dense vectors of real numbers where semantically similar words are mapped to nearby points. In this example we’ll use Keras to generate word embeddings for the Amazon Fine Foods Reviews dataset.
We will cover often-overlooked concepts vital to NLP, such as Byte Pair Encoding, and discuss how understanding them leads to better models.
With the rapid advance in NLP models we have outpaced out ability to measure just how good they are at human level language tasks. We need better NLP datasets now more than ever to both evaluate how good these models are and to be able to tweak them for out own business domains.
This article discusses GPT-2 and BERT models, as well using knowledge distillation to create highly accurate models with fewer parameters than their teachers
Table of Contents What Is Keras Core? Configuring Your Development Environment Let’s Talk about Keras! Going Beyond with Keras Core The Power of Keras Core: Expanding Your Deep Learning Horizons Show Me Some Code JAX Harnessing model.fit() Imports and Setup…
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What is Attention, and why is it used in state-of-the-art models? This article discusses the types of Attention and walks you through their implementations.
