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TensorFlow Machine Learning Cookbook

TensorFlow Machine Learning Cookbook

By : Nick McClure
3.7 (18)
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TensorFlow Machine Learning Cookbook

TensorFlow Machine Learning Cookbook

3.7 (18)
By: Nick McClure

Overview of this book

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.
Table of Contents (13 chapters)
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12
Index

Working with CBOW Embeddings


In this recipe we will implement the CBOW method of word2vec. It is very similar to the skip-gram method, except we are predicting a single target word from a surrounding window of context words.

Getting ready

In this recipe, we will implement the CBOW method of Word2vec. It is very similar to the skip-gram method, except we are predicting a single target word from a surrounding window of context words.

In the prior example, we treated each combination of window and target as a group of paired inputs and outputs, but with CBOW we will add the surrounding window embeddings together to get one embedding to predict the target word embedding:

Figure 5: A depiction of how the CBOW embedding data is created out of a window on an example sentence (window size = 1 on each side).

Most of the code will stay the same, except we will need to change how we create the embeddings and how we generate the data from the sentences.

To make the code easier to read, we have moved all the...

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