Deep Learning: Natural Language Processing in Python with Word2Vec: Word2Vec and Word Embeddings in Python and Theano (Deep Learning and Natural Language Processing Book 1)

Price: $2.99
(as of Dec 18,2024 02:43:01 UTC – Details)

ASIN ‏ : ‎ B01KQ0ZN0A
Publication date ‏ : ‎ August 19, 2016
Language ‏ : ‎ English
File size ‏ : ‎ 240 KB
Simultaneous device usage ‏ : ‎ Unlimited
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 47 pages

Deep Learning: Natural Language Processing in Python with Word2Vec

In this post, we will explore the concept of Word2Vec and Word Embeddings in Python and Theano. We will delve into the world of Deep Learning and Natural Language Processing, focusing on how Word2Vec can be used to create word embeddings for text data.

Word2Vec is a popular technique used in Natural Language Processing to map words to vectors in a continuous vector space. This allows us to capture the semantic relationships between words and represent them in a meaningful way that can be used in machine learning models.

In this book, we will cover the basics of Word2Vec and how it can be implemented in Python using libraries such as Gensim and TensorFlow. We will also explore how Word Embeddings can be used to improve the performance of NLP tasks such as sentiment analysis, text classification, and machine translation.

Whether you are new to Deep Learning and Natural Language Processing or looking to expand your knowledge in this field, this book is a valuable resource for anyone interested in leveraging Word2Vec and Word Embeddings in their projects. Stay tuned for more updates and insights on how you can harness the power of Deep Learning for NLP tasks.
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