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Learn how to solve real life problems using different methods like logic regression, random forests and SVM's with TensorFlow. About This Book Understand predictive analytics along with its challenges and best practices Embedded with assessments that will help you revise the concepts you have learned in this book Who This Book Is For This book is aimed at developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. What You Will Learn Learn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configuration Explore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analytics Solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics Dig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations Learn several examples of how to apply reinforcement learning algorithms for developing predictive models on real-life datasets In Detail Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google's brainchild, is immensely popular and extensively used for predictive analysis. This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features. This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to ...

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Build and deploy powerful neural network models using the latest Java deep learning libraries About This Book Understand DL with Java by implementing real-world projects Master implementations of various ANN models and build your own DL systems Develop applications using NLP, image classification, RL, and GPU processing Who This Book Is For If you are a data scientist, machine learning professional, or deep learning practitioner keen to expand your knowledge by delving into the practical aspects of deep learning with Java, then this book is what you need! Get ready to build advanced deep learning models to carry out complex numerical computations. Some basic understanding of machine learning concepts and a working knowledge of Java are required. What You Will Learn Master deep learning and neural network architectures Build real-life applications covering image classification, object detection, online trading, transfer learning, and multimedia analytics using DL4J and open-source APIs Train ML agents to learn from data using deep reinforcement learning Use factorization machines for advanced movie recommendations Train DL models on distributed GPUs for faster deep learning with Spark and DL4J Ease your learning experience through 69 FAQs In Detail Java is one of the most widely used programming languages. With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts. Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. You will see how to build several projects using different deep neural network architectures such as multilayer perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization Machines. You will get acquainted with popular deep and machine learning libraries for Java such as Deeplearning4j, Spark ML, and RankSys and you'll be able to use their features to build and deploy projects on distributed computing environments. You will then explore advanced domains such as transfer learning and deep reinforcement learning using the Java ecosystem, covering various real-world domains such as healthcare, NLP, image classification, and multimedia analytics with an easy-to-follow approach. Expert reviews and tips will follow every project to give you insights and hacks. By the end of this book, you will have stepped up your expertise when it comes to deep learning in Java, taking it beyond theory and be able to build ...

Java (Computer program language) --- Application program interfaces (Computer software) --- Machine learning. --- Application software --- Development.

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Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming. About This Book Explore machine learning techniques with prominent open source Scala libraries such as Spark ML, H2O, MXNet, Zeppelin, and DeepLearning4j Solve real-world machine learning problems by delving complex numerical computing with Scala functional programming in a scalable and faster way Cover all key aspects such as collection, storing, processing, analyzing, and evaluation required to build and deploy machine models on computing clusters using Scala Play framework. Who This Book Is For If you want to leverage the power of both Scala and Spark to make sense of Big Data, then this book is for you. If you are well versed with machine learning concepts and wants to expand your knowledge by delving into the practical implementation using the power of Scala, then this book is what you need! Strong understanding of Scala Programming language is recommended. Basic familiarity with machine Learning techniques will be more helpful. What You Will Learn Apply advanced regression techniques to boost the performance of predictive models Use different classification algorithms for business analytics Generate trading strategies for Bitcoin and stock trading using ensemble techniques Train Deep Neural Networks (DNN) using H2O and Spark ML Utilize NLP to build scalable machine learning models Learn how to apply reinforcement learning algorithms such as Q-learning for developing ML application Learn how to use autoencoders to develop a fraud detection application Implement LSTM and CNN models using DeepLearning4j and MXNet In Detail Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development. If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet. At the end,...

Scala (Computer program language) --- Machine learning. --- Electronic data processing.

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Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye! About This Book Learn Scala’s sophisticated type system that combines Functional Programming and object-oriented concepts Work on a wide array of applications, from simple batch jobs to stream processing and machine learning Explore the most common as well as some complex use-cases to perform large-scale data analysis with Spark Who This Book Is For Anyone who wishes to learn how to perform data analysis by harnessing the power of Spark will find this book extremely useful. No knowledge of Spark or Scala is assumed, although prior programming experience (especially with other JVM languages) will be useful to pick up concepts quicker. What You Will Learn Understand object-oriented & functional programming concepts of Scala In-depth understanding of Scala collection APIs Work with RDD and DataFrame to learn Spark’s core abstractions Analysing structured and unstructured data using SparkSQL and GraphX Scalable and fault-tolerant streaming application development using Spark structured streaming Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML Build clustering models to cluster a vast amount of data Understand tuning, debugging, and monitoring Spark applications Deploy Spark applications on real clusters in Standalone, Mesos, and YARN In Detail Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics u...

Big data. --- Scala (Computer program language) --- Spark (Electronic resource : Apache Software Foundation)