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Machine Learning Using TensorFlow Cookbook

Machine Learning Using TensorFlow Cookbook

By : Alexia Audevart, Konrad Banachewicz, Luca Massaron
4.9 (16)
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Machine Learning Using TensorFlow Cookbook

Machine Learning Using TensorFlow Cookbook

4.9 (16)
By: Alexia Audevart, Konrad Banachewicz, Luca Massaron

Overview of this book

The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow. This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression. Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems. With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios.
Table of Contents (15 chapters)
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5
Boosted Trees
11
Reinforcement Learning with TensorFlow and TF-Agents
13
Other Books You May Enjoy
14
Index

Processing dates

Dates are common in databases and, especially when processing the forecasting of future estimates (such as in sales forecasting), they can prove indispensable. Neural networks cannot process dates as they are, since they are often expressed as strings. Hence, you have to transform them by separating their numerical elements, and once you have split a date into its components, you have just numbers that can easily be dealt with by any neural network. Certain time elements, however, are cyclical (days, months, hours, days of the week) and lower and higher numbers are actually contiguous. Consequently, you need to use sine and cosine functions, which will render such cyclical numbers in a format that can be both understood and correctly interpreted by a DNN.

Getting ready

Since we need to code a class operating using the fit/transform operations that are typical of scikit-learn, we import the BaseEstimator and TransformerMixin classes from scikit-learn to inherit...

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Machine Learning Using TensorFlow Cookbook
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