In this chapter, we learned how to make predictions using TensorFlow. We studied the MNIST dataset and classification of models using this dataset. We came across the elements of DNN models and the process of building the DNN. Later, we progressed to study regression and classification with DNNs. We classified handwritten digits and learned more about building models in TensorFlow. This brings us to the end of this book! We learned how to use ensemble algorithms to produce accurate predictions. We applied various techniques to combine and build better models. We learned how to perform cross-validation efficiently. We also implemented various techniques to solve current issues in the domain of predictive analysis. And, the best part, we used the DNN models we built to solve classification and regression problems. This book has helped us implement various machine learning...
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Book Overview & Buying
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Table Of Contents
Mastering Predictive Analytics with scikit-learn and TensorFlow
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Mastering Predictive Analytics with scikit-learn and TensorFlow
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Overview of this book
Python is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems.
This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics.
By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis.
Table of Contents (7 chapters)
Preface
Free Chapter
Ensemble Methods for Regression and Classification
Cross-validation and Parameter Tuning
Working with Features
Introduction to Artificial Neural Networks and TensorFlow
Predictive Analytics with TensorFlow and Deep Neural Networks
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