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.
Take TensorFlow into production
Implement and fine-tune Transformer models for various NLP tasks
Apply reinforcement learning algorithms using the TF-Agents framework
Understand linear regression techniques and use Estimators to train linear models
Execute neural networks and improve predictions on tabular data
Master convolutional neural networks and recurrent neural networks through practical recipes
Basic familiarity with Python, linear algebra, statistics, and machine learning is necessary to make the most out of this book.