Deep Learning – A Practitioner’s Approach

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they even get very far. How can machine learning, especially deep nural networks, make a real difference in your organization? This hands-on guide note only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.

This book provides the fundamentals of deep learning (tuning, paralleization, vectoriztion, and building pipelines) that are valid for any library before introducing the open source DeepLearning4J(DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Haddop with DL4J.

Introduction to Machine Learning with Python

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

You will learn the steps necessary to create a successful machine learning application with Python and the scikit-learn library. The authors focus on the practical aspects of using machine learning algorithms, rather than math behind them. Familiarity with the numpy and matplotlib libraries will help you get even more from this book.

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