Graph Prompting Unlocking The Power Of Graph Neural Networks And Prompt Engineering For Advanced Ai Applications

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Graph Prompting: Unlocking the Power of Graph Neural Networks and Prompt Engineering for Advanced AI Applications

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 97 pages
Book Rating : 4./5 ( download)

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Book Synopsis Graph Prompting: Unlocking the Power of Graph Neural Networks and Prompt Engineering for Advanced AI Applications by : Anand Vemula

Download or read book Graph Prompting: Unlocking the Power of Graph Neural Networks and Prompt Engineering for Advanced AI Applications written by Anand Vemula and published by Anand Vemula. This book was released on with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Graph Prompting" explores the intersection of Graph Neural Networks (GNNs) and prompt engineering, providing a comprehensive guide on leveraging these technologies for advanced AI applications. The book is structured into several key sections, each delving into different aspects of graph-based AI. #### Fundamentals of Graph Theory The book begins by laying the foundation with essential concepts in graph theory, such as nodes, edges, types of graphs, and graph representations. It explains fundamental metrics like degree, centrality, and clustering coefficients, and covers important algorithms for pathfinding and connectivity. #### Introduction to Prompting The next section introduces prompting in AI, particularly for large language models (LLMs). It covers the basics of prompt engineering, types of prompts (instruction-based, task-based), and design principles. Techniques like contextual prompting, chain-of-thought prompting, and few-shot/zero-shot prompting are discussed, providing practical examples and use cases. #### Graph Neural Networks (GNNs) A comprehensive overview of GNNs follows, detailing their architecture and applications. Key models like Graph Convolutional Networks (GCNs), GraphSAGE, and Graph Attention Networks (GATs) are explained with examples. The section also covers advanced GNN models, including transformer-based graph models and attention mechanisms. #### Graph Prompting for LLMs This section focuses on integrating GNNs with LLMs. It explores techniques for using graph embeddings in prompting, enhancing the capabilities of LLMs in various tasks such as recommendation systems, anomaly detection, and question answering. Practical applications and case studies demonstrate the effectiveness of these integrations. #### Ethics and Fairness in Graph Prompting Ethical considerations are crucial, and the book addresses biases in graph data and fairness in graph algorithms. It discusses the ethical implications of using graph data and provides strategies to ensure fairness and mitigate biases. #### Practical Applications and Case Studies The book highlights real-world applications of graph prompting in healthcare, social networks, and recommendation systems. Each case study showcases the practical benefits and challenges of implementing these technologies in different domains. #### Implementation Guides and Tools For practitioners, the book offers step-by-step implementation guides, using popular libraries like PyTorch Geometric and DGL. Example projects provide hands-on experience, helping readers apply the concepts discussed. #### Future Trends and Conclusion The book concludes with a look at future trends in graph prompting, including scalable GNNs, graph-based reinforcement learning, and ethical AI. It encourages continuous exploration and adaptation to leverage the full potential of graph-based AI technologies. Overall, "Graph Prompting" is a detailed and practical guide, offering valuable insights and tools for leveraging GNNs and prompt engineering to advance AI applications across various domains.

Advances in Graph Neural Networks

Author : Chuan Shi
Publisher : Springer Nature
ISBN 13 : 3031161742
Total Pages : 207 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Advances in Graph Neural Networks by : Chuan Shi

Download or read book Advances in Graph Neural Networks written by Chuan Shi and published by Springer Nature. This book was released on 2022-11-16 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications.

Graph Representation Learning

Author : William L. William L. Hamilton
Publisher : Springer Nature
ISBN 13 : 3031015886
Total Pages : 141 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Graph Representation Learning by : William L. William L. Hamilton

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Responsible Graph Neural Networks

Author : Mohamed Abdel-Basset
Publisher : CRC Press
ISBN 13 : 1000871177
Total Pages : 324 pages
Book Rating : 4.0/5 (8 download)

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Book Synopsis Responsible Graph Neural Networks by : Mohamed Abdel-Basset

Download or read book Responsible Graph Neural Networks written by Mohamed Abdel-Basset and published by CRC Press. This book was released on 2023-06-05 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: More frequent and complex cyber threats require robust, automated, and rapid responses from cyber-security specialists. This book offers a complete study in the area of graph learning in cyber, emphasizing graph neural networks (GNNs) and their cyber-security applications. Three parts examine the basics, methods and practices, and advanced topics. The first part presents a grounding in graph data structures and graph embedding and gives a taxonomic view of GNNs and cyber-security applications. The second part explains three different categories of graph learning, including deterministic, generative, and reinforcement learning and how they can be used for developing cyber defense models. The discussion of each category covers the applicability of simple and complex graphs, scalability, representative algorithms, and technical details. Undergraduate students, graduate students, researchers, cyber analysts, and AI engineers looking to understand practical deep learning methods will find this book an invaluable resource.

Graph Data Mining

Author : Qi Xuan
Publisher : Springer Nature
ISBN 13 : 981162609X
Total Pages : 256 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Graph Data Mining by : Qi Xuan

Download or read book Graph Data Mining written by Qi Xuan and published by Springer Nature. This book was released on 2021-07-15 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph data is powerful, thanks to its ability to model arbitrary relationship between objects and is encountered in a range of real-world applications in fields such as bioinformatics, traffic network, scientific collaboration, world wide web and social networks. Graph data mining is used to discover useful information and knowledge from graph data. The complications of nodes, links and the semi-structure form present challenges in terms of the computation tasks, e.g., node classification, link prediction, and graph classification. In this context, various advanced techniques, including graph embedding and graph neural networks, have recently been proposed to improve the performance of graph data mining. This book provides a state-of-the-art review of graph data mining methods. It addresses a current hot topic – the security of graph data mining – and proposes a series of detection methods to identify adversarial samples in graph data. In addition, it introduces readers to graph augmentation and subgraph networks to further enhance the models, i.e., improve their accuracy and robustness. Lastly, the book describes the applications of these advanced techniques in various scenarios, such as traffic networks, social and technical networks, and blockchains.

Graph Machine Learning

Author : Claudio Stamile
Publisher : Packt Publishing Ltd
ISBN 13 : 1800206755
Total Pages : 338 pages
Book Rating : 4.8/5 (2 download)

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Book Synopsis Graph Machine Learning by : Claudio Stamile

Download or read book Graph Machine Learning written by Claudio Stamile and published by Packt Publishing Ltd. This book was released on 2021-06-25 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features Implement machine learning techniques and algorithms in graph data Identify the relationship between nodes in order to make better business decisions Apply graph-based machine learning methods to solve real-life problems Book Description Graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You'll then learn all you need to know about the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you'll be taken through real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. You'll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications. What you will learn Write Python scripts to extract features from graphs Distinguish between the main graph representation learning techniques Learn how to extract data from social networks, financial transaction systems, for text analysis, and more Implement the main unsupervised and supervised graph embedding techniques Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more Deploy and scale out your application seamlessly Who this book is for This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. It will also be useful for machine learning developers or anyone who wants to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required, alongside a solid understanding of ML basics. You'll also need intermediate-level Python programming knowledge to get started with this book.

The Practitioner's Guide to Graph Data

Author : Denise Gosnell
Publisher : O'Reilly Media
ISBN 13 : 1492044040
Total Pages : 420 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis The Practitioner's Guide to Graph Data by : Denise Gosnell

Download or read book The Practitioner's Guide to Graph Data written by Denise Gosnell and published by O'Reilly Media. This book was released on 2020-03-20 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together. By working with concepts from graph theory, database schema, distributed systems, and data analysis, you’ll arrive at a unique intersection known as graph thinking. Authors Denise Koessler Gosnell and Matthias Broecheler show data engineers, data scientists, and data analysts how to solve complex problems with graph databases. You’ll explore templates for building with graph technology, along with examples that demonstrate how teams think about graph data within an application. Build an example application architecture with relational and graph technologies Use graph technology to build a Customer 360 application, the most popular graph data pattern today Dive into hierarchical data and troubleshoot a new paradigm that comes from working with graph data Find paths in graph data and learn why your trust in different paths motivates and informs your preferences Use collaborative filtering to design a Netflix-inspired recommendation system

Advanced Operators for Graph Neural Networks

Author : Yao Ma
Publisher :
ISBN 13 :
Total Pages : 133 pages
Book Rating : 4.5/5 (381 download)

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Book Synopsis Advanced Operators for Graph Neural Networks by : Yao Ma

Download or read book Advanced Operators for Graph Neural Networks written by Yao Ma and published by . This book was released on 2021 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graphs, which encode pairwise relations between entities, are a kind of universal data structure for many real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. For example, friend recommendation in social networks can be regarded as a link prediction task and predicting properties of chemical compounds can be treated as a graph classification task. An essential step to facilitate these tasks is to learn vector representations either for nodes or the entire graphs. Given its great success of representation learning in images and text, deep learning offers great promise for graphs. However, compared to images and text, deep learning on graphs faces immense challenges. Graphs are irregular where nodes are unordered and each of them can have a distinct number of neighbors. Thus, traditional deep learning models cannot be directly applied to graphs, which calls for dedicated efforts for designing novel deep graph models. To help meet this pressing demand, we developed and investigated novel GNN algorithms to generalize deep learning techniques to graph-structured data. Two key operations in GNNs are the graph filtering operation, which aims to refine node representations; and the graph pooling operation, which aims to summarize node representations to obtain a graph representation. In this thesis, we provide deep understandings or develop novel algorithms for these two operations from new perspectives. For graph filtering operations, we propose a unified framework from the perspective of graph signal denoising, which demonstrates that most existing graph filtering operations are conducting feature smoothing. Then, we further investigate what information typical graph filtering operations can capture and how they can be understood beyond feature smoothing. For graph pooling operations, we study the procedure of pooling from the perspective of graph spectral theory and present a novel graph pooling operation. We also propose a technique to downsample nodes considering both mode importance and representativeness, which leads to a novel graph pooling operation.

Introduction to Graph Signal Processing

Author : Antonio Ortega
Publisher : Cambridge University Press
ISBN 13 : 1108640176
Total Pages : pages
Book Rating : 4.1/5 (86 download)

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Book Synopsis Introduction to Graph Signal Processing by : Antonio Ortega

Download or read book Introduction to Graph Signal Processing written by Antonio Ortega and published by Cambridge University Press. This book was released on 2022-06-09 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: An intuitive and accessible text explaining the fundamentals and applications of graph signal processing. Requiring only an elementary understanding of linear algebra, it covers both basic and advanced topics, including node domain processing, graph signal frequency, sampling, and graph signal representations, as well as how to choose a graph. Understand the basic insights behind key concepts and learn how graphs can be associated to a range of specific applications across physical, biological and social networks, distributed sensor networks, image and video processing, and machine learning. With numerous exercises and Matlab examples to help put knowledge into practice, and a solutions manual available online for instructors, this unique text is essential reading for graduate and senior undergraduate students taking courses on graph signal processing, signal processing, information processing, and data analysis, as well as researchers and industry professionals.

Information Retrieval and Natural Language Processing

Author : Sheetal S. Sonawane
Publisher : Springer Nature
ISBN 13 : 981169995X
Total Pages : 186 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Information Retrieval and Natural Language Processing by : Sheetal S. Sonawane

Download or read book Information Retrieval and Natural Language Processing written by Sheetal S. Sonawane and published by Springer Nature. This book was released on 2022-02-22 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives a comprehensive view of graph theory in informational retrieval (IR) and natural language processing(NLP). This book provides number of graph techniques for IR and NLP applications with examples. It also provides understanding of graph theory basics, graph algorithms and networks using graph. The book is divided into three parts and contains nine chapters. The first part gives graph theory basics and graph networks, and the second part provides basics of IR with graph-based information retrieval. The third part covers IR and NLP recent and emerging applications with case studies using graph theory. This book is unique in its way as it provides a strong foundation to a beginner in applying mathematical structure graph for IR and NLP applications. All technical details that include tools and technologies used for graph algorithms and implementation in Information Retrieval and Natural Language Processing with its future scope are explained in a clear and organized format.

On the Predictive Power of Graph Neural Networks

Author : Weihua Hu
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (135 download)

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Book Synopsis On the Predictive Power of Graph Neural Networks by : Weihua Hu

Download or read book On the Predictive Power of Graph Neural Networks written by Weihua Hu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph Neural Networks (GNNs) are a class of deep learning models for making predictions on graph-structured data. Many different GNN models have been proposed to achieve promising empirical performance. However, their architectural designs were ad-hoc, and their theoretical understanding remained limited. Moreover, these models were developed on small graph benchmark datasets, which altogether limit the development of powerful GNNs for real-world prediction tasks over graphs. In this thesis, we aim to build powerful predictive GNNs by understanding, improving, and benchmarking the predictive power of GNNs--the ability of GNNs to make accurate predictions over graphs. This thesis consists of three parts. In Part I, we develop a theoretical framework for understanding the predictive power of GNNs. We specifically focus on the expressive power, asking whether GNNs can express desired functions over graphs. We use our theoretical framework to provide insight into whether a given GNN is powerful enough to model the ground-truth target function that underlies the data. We also propose a maximally-expressive GNN model that can provably model most functions over graphs. Equipped with the framework to design expressive GNN models, in Part II, we move on to improve their predictive power on unseen/unlabeled data, i.e., improve the generalization power of GNNs. Motivated by real-world applications, we develop methods for improving the generalization power of GNNs under two common limited data scenarios: limited labeled data and limited edge connectivity. Finally, in Part III, we create new graph benchmark datasets to resolve the issues with the existing benchmarks and to engage the community toward improving the predictive power of GNNs. We present the Open Graph Benchmark (OGB) and OGB-LSC, a collection of challenging, realistic, and large-scale benchmark datasets for machine learning on graphs. We discuss the impact our benchmarks have had in advancing the predictive power of GNNs and conclude with future challenges of applying GNNs to real-world prediction tasks.

The Practitioner's Guide to Graph Data

Author : Denise Gosnell
Publisher : O'Reilly Media
ISBN 13 : 9781492044079
Total Pages : 250 pages
Book Rating : 4.0/5 (44 download)

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Book Synopsis The Practitioner's Guide to Graph Data by : Denise Gosnell

Download or read book The Practitioner's Guide to Graph Data written by Denise Gosnell and published by O'Reilly Media. This book was released on 2020-01-04 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: How do you apply graph thinking to solve complex problems? With this practical guide, data scientists will learn how to think about data as a graph and determine if graph technology is right for your company. You'll learn techniques for building scalable, real-time, and multimodel architectures that solve complex problems with graph data. Authors Denise Koessler Gosnell and Matthias Broecheler show you how companies today are successfully applying graph thinking in distributed production environments. You'll also learn the Graph Schema Language, a set of terminology and visual illustrations to normalize how graph practitioners communicate conceptual graph models, graph schema, and graph database design.

Graph Representation Learning

Author : Hamilton William L. (author)
Publisher :
ISBN 13 : 9781636391205
Total Pages : pages
Book Rating : 4.3/5 (912 download)

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Book Synopsis Graph Representation Learning by : Hamilton William L. (author)

Download or read book Graph Representation Learning written by Hamilton William L. (author) and published by . This book was released on 1901 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Graph Neural Networks and Its Applications

Author : Pau Rodríguez Esmerats
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (122 download)

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Book Synopsis Graph Neural Networks and Its Applications by : Pau Rodríguez Esmerats

Download or read book Graph Neural Networks and Its Applications written by Pau Rodríguez Esmerats and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This project will explore some of the most prominent Graph Neural Network variants and apply them to two tasks: approximation of the community detection Girvan-Newman algorithm and compiled code snippet classification.

Artificial Intelligence in Healthcare

Author : Adam Bohr
Publisher : Academic Press
ISBN 13 : 0128184396
Total Pages : 385 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Artificial Intelligence in Healthcare by : Adam Bohr

Download or read book Artificial Intelligence in Healthcare written by Adam Bohr and published by Academic Press. This book was released on 2020-06-21 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data

Out-of-distribution Generalization in Graph Neural Networks

Author : Yiqi Wang
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.3/5 (584 download)

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Book Synopsis Out-of-distribution Generalization in Graph Neural Networks by : Yiqi Wang

Download or read book Out-of-distribution Generalization in Graph Neural Networks written by Yiqi Wang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graphs are one of the most natural representations of many real-world data, such as social networks, chemical molecules, and transportation networks. Graph neural networks (GNNs) are deep neural networks (DNNs) that are specially designed for graphs and have aroused great research interest. Recently, GNNs have been theoretically and empirically proven to be effective in learning graph representations and have been widely applied in many scenarios, such as recommendation and drug discovery. Despite its great success in numerous graph-related tasks, GNNs still face a tremendous challenge in terms of out-of-distribution generalization. Specifically, it has been observed that significant performance gaps for GNNs exist between the training graph set and the test graph set in some graph-related tasks. In addition, graph samples can be very diverse, even though coming from the same dataset. They can be different from each other in not only node attributes but graph structures, which makes the out-of-distribution generalization problem in GNNs more challenging and complex than that in traditional deep learning-based methods. Apart from the out-of-distribution generalization problem, GNNs also come across other kinds of challenges when applied in different application scenarios, such as data sparsity and knowledge transfer in the recommendation task. In this dissertation, we aim at alleviating the out-of-distribution generalization problem in GNNs. In particular, two novel frameworks are proposed to improve GNN's out-of-distribution generalization ability from two perspectives, i.e., a novel training perspective, and an advanced learning perspective. Meanwhile, we design a novel GNN-based method to solve the data sparsity challenge in the recommendation application. In addition, we propose an adaptive pre-training framework based on the new GNN-based recommendation method and thus increase the abilities of GNNs in terms of generalization and knowledge transfer in the real-world application of recommendations.

Artificial Intelligence with Python

Author : Prateek Joshi
Publisher : Packt Publishing Ltd
ISBN 13 : 1786469677
Total Pages : 437 pages
Book Rating : 4.7/5 (864 download)

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Book Synopsis Artificial Intelligence with Python by : Prateek Joshi

Download or read book Artificial Intelligence with Python written by Prateek Joshi and published by Packt Publishing Ltd. This book was released on 2017-01-27 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.