Machine Learning Using TensorFlow Cookbook: Over 60 recipes on machine learning using deep learning solutions from Kaggle Masters and Google Developer Experts

Master TensorFlow to create powerful machine learning algorithms, with valuable insights on Keras, Boosted Trees, Tabular Data, Transformers, Reinforcement Learning and moreKey FeaturesWork with the latest code and examples for TensorFlow 2Get to grips with the fundamentals including variables, matrices, and data sourcesLearn advanced deep learning techniques to make your algorithms faster and more accurateBook DescriptionThe independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data.

You will work through 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 begins by introducing you to the fundamentals of the TensorFlow library, including variables, matrices, and various data sources.

You'll then take a deep dive into some real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and for regression to provide a baseline for tabular data problems.

As you progress, you'll explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be applied to computer vision and natural language processing (NLP) problems.

Once you are familiar with the TensorFlow ecosystem, the final chapter will teach you how to take a project to production.

By the end of this machine learning book, you will be proficient in using TensorFlow 2.

You'll also understand deep learning from the fundamentals and be able to implement machine learning algorithms in real-world scenarios.What you will learnGrasp linear regression techniques with TensorFlowUse Estimators to train linear models and boosted trees for classification or regressionExecute neural networks and improve predictions on tabular dataMaster convolutional neural networks and recurrent neural networks through practical recipesApply reinforcement learning algorithms using the TF-Agents frameworkImplement and fine-tune Transformer models for various NLP tasksTake TensorFlow into productionWho this book is forIf you are a data scientist or a machine learning engineer, and you want to skip detailed theoretical explanations in favor of building production-ready machine learning models using TensorFlow, this book is for you.Basic familiarity with Python, linear algebra, statistics, and machine learning is necessary to make the most out of this book.