Hands-On Artificial Intelligence with Java for Beginners: Build intelligent apps using machine learning and deep learning with Deeplearning4j
- Length: 144 pages
- Edition: 1
- Language: English
- Publisher: Packt Publishing
- Publication Date: 2018-08-31
- ISBN-10: 178953755X
- ISBN-13: 9781789537550
- Sales Rank: #2557077 (See Top 100 Books)
Build, train, and deploy intelligent applications using Java libraries
Key Features
- Leverage the power of Java libraries to build smart applications
- Build and train deep learning models for implementing artificial intelligence
- Learn various algorithms to automate complex tasks
Book Description
Artificial intelligence (AI) is increasingly in demand as well as relevant in the modern world, where everything is driven by technology and data. AI can be used for automating systems or processes to carry out complex tasks and functions in order to achieve optimal performance and productivity.
Hands-On Artificial Intelligence with Java for Beginners begins by introducing you to AI concepts and algorithms. You will learn about various Java-based libraries and frameworks that can be used in implementing AI to build smart applications.
In addition to this, the book teaches you how to implement easy to complex AI tasks, such as genetic programming, heuristic searches, reinforcement learning, neural networks, and segmentation, all with a practical approach.
By the end of this book, you will not only have a solid grasp of AI concepts, but you’ll also be able to build your own smart applications for multiple domains.
What you will learn
- Leverage different Java packages and tools such as Weka, RapidMiner, and Deeplearning4j, among others
- Build machine learning models using supervised and unsupervised machine learning techniques
- Implement different deep learning algorithms in Deeplearning4j and build applications based on them
- Study the basics of heuristic searching and genetic programming
- Differentiate between syntactic and semantic similarity among texts
- Perform sentiment analysis for effective decision making with LingPipe
Who this book is for
Hands-On Artificial Intelligence with Java for Beginners is for Java developers who want to learn the fundamentals of artificial intelligence and extend their programming knowledge to build smarter applications.
Title Page Copyright and Credits Hands-On Artificial Intelligence with Java for Beginners Packt Upsell Why subscribe? PacktPub.com Contributors About the author Packt is searching for authors like you Preface Who this book is for What this book covers To get the most out of this book Download the example code files Conventions used Get in touch Reviews Introduction to Artificial Intelligence and Java What is machine learning? Differences between classification and regression Installing JDK and JRE Setting up the NetBeans IDE Importing Java libraries and exporting code in projects as a JAR file Summary Exploring Search Algorithms An introduction to searching Implementing Dijkstra's search Understanding the notion of heuristics A brief introduction to the A* algorithm Implementing an A* algorithm Summary AI Games and the Rule-Based System Introducing the min-max algorithm Implementing an example min-max algorithm Installing Prolog An introduction to rule-based systems with Prolog Setting up Prolog with Java Executing Prolog queries using Java Summary Interfacing with Weka An introduction to Weka Installing and interfacing with Weka Calling the Weka environment into Java Reading and writing datasets Converting datasets Converting an ARFF file to a CSV file Converting a CSV file to an ARFF file Summary Handling Attributes Filtering attributes Discretizing attributes Attribute selection Summary Supervised Learning Developing a classifier Model evaluation Making predictions Loading and saving models Summary Semi-Supervised and Unsupervised Learning Working with k-means clustering Evaluating a clustering model An introduction to semi-supervised learning The difference between unsupervised and semi-supervised learning Self-training and co-training machine learning models Downloading a semi-supervised package Creating a classifier for semi-supervised models Making predictions with semi-supervised machine learning models Summary Other Books You May Enjoy Leave a review - let other readers know what you think
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