Take a look at our Artificial Intelligence books. Shulph carries a great selection of Artificial Intelligence books, and we are always adding more.
Create end-to-end systems that can power robots with artificial vision and deep learning techniques Key Features Study ROS, the main development framework for robotics, in detail Learn all about convolutional neural networks, recurrent neural networks, and robotics Create a chatbot to interact with the robot Book Description Artificial Vision and Language Processing for Robotics begins by discussing the theory behind robots. You'll compare different methods used to work with robots and explore computer vision, its algorithms, and limits. You'll then learn how to control the robot with natural language processing commands. You'll study Word2Vec and GloVe embedding techniques, non-numeric data, recurrent neural network (RNNs), and their advanced models. You'll create a simple Word2Vec model with Keras, as well as build a convolutional neural network (CNN) and improve it with data augmentation and transfer learning. You'll study the ROS and build a conversational agent to manage your robot. You'll also integrate your agent with the ROS and convert an image to text and text to speech. You'll learn to build an object recognition system using a video. By the end of this book, you'll have the skills you need to build a functional application that can integrate with a ROS to extract useful information about your environment. What you will learn Explore the ROS and build a basic robotic system Understand the architecture of neural networks Identify conversation intents with NLP techniques Learn and use the embedding with Word2Vec and GloVe Build a basic CNN and improve it using generative models Use deep learning to implement artificial intelligence(AI)and object recognition Develop a simple object recognition system using CNNs Integrate AI with ROS to enable your robot to recognize objects Who this book is for Artificial Vision and Language Processing for Robotics is for robotics engineers who want to learn how to integrate computer vision and deep learning techniques to create complete robotic systems. It will prove beneficial to you if you have working knowledge of Python and a background in deep learning. Knowledge of the ROS is a plus.
Cyber Minds brings together an unrivalled panel of international experts who offer their insights into current cybersecurity issues in the military, business, and government. Key Features Explore the latest developments in cybersecurity Hear expert insight from the industry's top practitioners Dive deep into cyber threats in business, government, and military Book Description Shira Rubinoff's Cyber Minds brings together the top authorities in cybersecurity to discuss the emergent threats that face industries, societies, militaries, and governments today. With new technology threats, rising international tensions, and state-sponsored cyber attacks, cybersecurity is more important than ever. Cyber Minds serves as a strategic briefing on cybersecurity and data safety, collecting expert insights from sector security leaders, including: General Gregory Touhill, former Federal Chief Information Security Officer of the United States Kevin L. Jackson, CEO and Founder, GovCloud Mark Lynd, Digital Business Leader, NETSYNC Joseph Steinberg, Internet Security advisor and thought leader Jim Reavis, Co-Founder and CEO, Cloud Security Alliance Dr. Tom Kellerman, Chief Cybersecurity Officer for Carbon Black Inc and Vice Chair of Strategic Cyber Ventures Board Mary Ann Davidson, Chief Security Officer, Oracle Dr. Sally Eaves, Emergent Technology CTO, Global Strategy Advisor – Blockchain AI FinTech, Social Impact award winner, keynote speaker and author Dr. Guenther Dobrauz, Partner with PwC in Zurich and Leader of PwC Legal Switzerland Barmak Meftah, President, AT&T Cybersecurity Cleve Adams, CEO, Site 1001 (AI and big data based smart building company) Ann Johnson, Corporate Vice President – Cybersecurity Solutions Group, Microsoft Barbara Humpton, CEO, Siemens USA Businesses and states depend on effective cybersecurity. This book will help you to arm and inform yourself on what you need to know to keep your business – or your country – safe. What you will learn The threats and opportunities presented by AI How to mitigate social engineering and other human threats Developing cybersecurity strategies for the cloud Major data breaches, their causes, consequences, and key takeaways Blockchain applications for cybersecurity Implications of IoT and how to secure IoT services The role of security in cyberterrorism and state-sponsored cyber attacks Who this book is for This book is essential reading for business leaders, the C-Suite, board members, IT decision makers within an organization, and anyone with a responsibility for cybersecurity.
Leading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business. It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working. Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI.
Drives next generation path with latest design techniques and methods in the fields of AI and Deep Learning Key Features - Extensive examples of Machine Learning and Deep Learning principles. - Includes graphical demonstrations and visual tutorials for various libraries, configurations, and settings. - Numerous use cases with the code snippets and examples are presented. Description 'Essentials of Deep Learning and AI' curates the essential knowledge of working on deep neural network techniques and advanced machine learning concepts. This book is for those who want to know more about how deep neural networks work and advanced machine learning principles including real-world examples. This book includes implemented code snippets and step-by-step instructions for how to use them. You'll be amazed at how SciKit-Learn, Keras, and TensorFlow are used in AI applications to speed up the learning process and produce superior results. With the help of detailed examples and code templates, you'll be running your scripts in no time. You will practice constructing models and optimise performance while working in an AI environment. Readers will be able to start writing their programmes with confidence and ease. Experts and newcomers alike will have access to advanced methodologies. For easier reading, concept explanations are presented straightforwardly, with all relevant facts included. What you will learn - Learn feature engineering using a variety of autoencoders, CNNs, and LSTMs. - Get to explore Time Series, Computer Vision and NLP models with insightful examples. - Dive deeper into Activation and Loss functions with various scenarios. - Get the experience of Deep Learning and AI across IoT, Telecom, and Health Care. - Build a strong foundation around AI, ML and Deep Learning principles and key concepts. Who this book is for This book targets Machine Learning Engineers, Data Scientists, Data Engineers, Business Intelligence Analysts, and Software Developers who wish to gain a firm grasp on the fundamentals of Deep Learning and Artificial Intelligence. Readers should have a working knowledge of computer programming concepts. Table of Contents 1. Introduction 2. Supervised Machine Learning 3. System Analysis with Machine Learning/Un-Supervised Learning 4. Feature Engineering 5. Classification, Clustering, Association Rules, and Regression 6. Time Series Analysis 7. Data Cleanup, Characteristics and Feature Selection 8. Ensemble Model Development 9. Design with Deep Learning 10. Design with Multi Layered Perceptron (MLP) 11. Long Short Term Memory Networks 12. Autoencoders 13. Applications of Machine Learning and Deep Learning 14. Emerging and Future Technologies.
A complete guide to build a better Chatbots Key Features Concept of artificial intelligence (AI) and machine learning How AI is involved in creating chatbots What are chatbots Chatbot development Live chatting Create chatbot with technologies such as Amazon Lex, Google Dialogflow, AWS Lambda, Microsoft Bot Framework, and Azure Deploy and talk to your bot Description This book makes you familiar with the concept of the chatbot. It explains what chatbot is, how does a chatbot work, and what exactly is the need for a chatbot in today’s era? It focuses on creating a bot using Amazon’s Lex service and getting the bot deployed on Facebook messenger for live chatting. This book will train you on how to create a chatbot using Google’s Dialogflow and test the bot in Dialogflow console. It also demonstrates how to create a custom chatbot using Microsoft’s bot framework and enable the webhooks in Dialogflow and return the response from the custom bot to Dialogflow intents as a fulfilment response. What You Will Learn Learn the concept of chatbot Learn how chatbots and AI work hand in hand Learn the concept of machine learning in chatbots Get familiar with chatbot services such as Amazon’s Lex and Google’s Dialogflow Learn how to write an AWS Lambda function Learn what webhooks are Learn about Microsoft’s Bot Framework Write your own custom chatbot Deploy the chatbot on Facebook Messenger, Google Assistant, and Slack Live chatting with your own chatbot Who This Book Is For The developers, architects, and software/technology enthusiasts who are keen to learn the cutting-edge technologies and want to get a hands-on experience on AI by creating their own custom chatbots. Organizations, small companies, service-based/product-based setups which want to learn how to create a basic chatbot on their website and on social media to get more leads and reach to the end user for their business. Students, if they are seeking something where they can create and integrate the real-time chatbots in their projects. Table of Contents Section 1: The Concept What are Chatbots? How Chatbot Works What is the Need for a Chatbot? Conversational Flow? Section 2: Creating a Chatbot Using Amazon Lex Amazon Lex and AWS Account Create Bot Using Amazon Lex AWS Lambda Function Slots Error Handling Deploy the Bot on Facebook Messenger Live Chatbot on Facebook Section 3: Creating a Chatbot Using Dialogflow API and Microsoft’s Bot Framework Technical Requirements Dialogflow Account Creating a Bot in Dialogflow Dialogflow Console Integrating the Bot with Slack Chatbot Using Microsoft Bot Framework Publishing the Bot from Visual Studio to Azure Register the Bot Dialogflow.v2 SDK Webhooks in Dialogflow Testing the Bot Deploy the Chatbot in Facebook Messenger Live Chatbot on Facebook Deploy the Chatbot in Slack Future of Chatbots About the Author Akhil Mittal is two times Microsoft MVP (Most Valuable Professional) firstly awarded in 2016 continued in 2017 in Visual Studio and Technologies category, C# Corner MVP since 2013, Code Project MVP since 2014, a blogger, author and likes to write/read technical articles, blogs and books. He works as a Sr. Consultant with Magic Edtech (https://www.magicedtech.com/) which is recognized as a global leader in delivering end to end learning solutions. He has an experience of around 12 years in developing, designing, architecting enterprises level applications primarily in Microsoft Technologies. He has a diverse experience in working on cutting edge technologies that include Microsoft Stack, AI, Machine Learning and Cloud computing. Akhil is an MCP (Microsoft Certified Professional) in Web Applications and Dot Net Framework. linkedin: linkedin.com/in/akhilmittal
Grasp the fundamentals of Artificial Intelligence and build your own intelligent systems with ease Key Features Enter the world of AI with the help of solid concepts and real-world use cases Explore AI components to build real-world automated intelligence Become well versed with machine learning and deep learning concepts Book Description Virtual Assistants, such as Alexa and Siri, process our requests, Google's cars have started to read addresses, and Amazon's prices and Netflix's recommended videos are decided by AI. Artificial Intelligence is one of the most exciting technologies and is becoming increasingly significant in the modern world. Hands-On Artificial Intelligence for Beginners will teach you what Artificial Intelligence is and how to design and build intelligent applications. This book will teach you to harness packages such as TensorFlow in order to create powerful AI systems. You will begin with reviewing the recent changes in AI and learning how artificial neural networks (ANNs) have enabled more intelligent AI. You'll explore feedforward, recurrent, convolutional, and generative neural networks (FFNNs, RNNs, CNNs, and GNNs), as well as reinforcement learning methods. In the concluding chapters, you'll learn how to implement these methods for a variety of tasks, such as generating text for chatbots, and playing board and video games. By the end of this book, you will be able to understand exactly what you need to consider when optimizing ANNs and how to deploy and maintain AI applications. What you will learn Use TensorFlow packages to create AI systems Build feedforward, convolutional, and recurrent neural networks Implement generative models for text generation Build reinforcement learning algorithms to play games Assemble RNNs, CNNs, and decoders to create an intelligent assistant Utilize RNNs to predict stock market behavior Create and scale training pipelines and deployment architectures for AI systems Who this book is for This book is designed for beginners in AI, aspiring AI developers, as well as machine learning enthusiasts with an interest in leveraging various algorithms to build powerful AI applications.
Build smart cybersecurity systems with the power of machine learning and deep learning to protect your corporate assets Key Features Identify and predict security threats using artificial intelligence Develop intelligent systems that can detect unusual and suspicious patterns and attacks Learn how to test the effectiveness of your AI cybersecurity algorithms and tools Book Description Today's organizations spend billions of dollars globally on cybersecurity. Artificial intelligence has emerged as a great solution for building smarter and safer security systems that allow you to predict and detect suspicious network activity, such as phishing or unauthorized intrusions. This cybersecurity book presents and demonstrates popular and successful AI approaches and models that you can adapt to detect potential attacks and protect your corporate systems. You'll learn about the role of machine learning and neural networks, as well as deep learning in cybersecurity, and you'll also learn how you can infuse AI capabilities into building smart defensive mechanisms. As you advance, you'll be able to apply these strategies across a variety of applications, including spam filters, network intrusion detection, botnet detection, and secure authentication. By the end of this book, you'll be ready to develop intelligent systems that can detect unusual and suspicious patterns and attacks, thereby developing strong network security defenses using AI. What you will learn Detect email threats such as spamming and phishing using AI Categorize APT, zero-days, and polymorphic malware samples Overcome antivirus limits in threat detection Predict network intrusions and detect anomalies with machine learning Verify the strength of biometric authentication procedures with deep learning Evaluate cybersecurity strategies and learn how you can improve them Who this book is for If you're a cybersecurity professional or ethical hacker who wants to build intelligent systems using the power of machine learning and AI, you'll find this book useful. Familiarity with cybersecurity concepts and knowledge of Python programming is essential to get the most out of this book.
Build smarter systems by combining artificial intelligence and the Internet of Things—two of the most talked about topics today Key Features Leverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT data Process IoT data and predict outcomes in real time to build smart IoT models Cover practical case studies on industrial IoT, smart cities, and home automation Book Description There are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter. This book starts by covering the process of gathering and preprocessing IoT data gathered from distributed sources. You will learn different AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing to build smart IoT systems. You will also leverage the power of AI to handle real-time data coming from wearable devices. As you progress through the book, techniques for building models that work with different kinds of data generated and consumed by IoT devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of IoT solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, TensorFlow and Keras, to build different kinds of smart AI models. By the end of this book, you will be able to build smart AI-powered IoT apps with confidence. What you will learn Apply different AI techniques including machine learning and deep learning using TensorFlow and Keras Access and process data from various distributed sources Perform supervised and unsupervised machine learning for IoT data Implement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platforms Forecast time-series data using deep learning methods Implementing AI from case studies in Personal IoT, Industrial IoT, and Smart Cities Gain unique insights from data obtained from wearable devices and smart devices Who this book is for If you are a data science professional or a machine learning developer looking to build smart systems for IoT, Hands-On Artificial Intelligence for IoT is for you. If you want to learn how popular artificial intelligence (AI) techniques can be used in the Internet of Things domain, this book will also be of benefit. A basic understanding of machine learning concepts will be required to get the best out of this book.
Make your searches more responsive and smarter by applying Artificial Intelligence to it Key Features Enter the world of Artificial Intelligence with solid concepts and real-world use cases Make your applications intelligent using AI in your day-to-day apps and become a smart developer Design and implement artificial intelligence in searches Book Description With the emergence of big data and modern technologies, AI has acquired a lot of relevance in many domains. The increase in demand for automation has generated many applications for AI in fields such as robotics, predictive analytics, finance, and more. In this book, you will understand what artificial intelligence is. It explains in detail basic search methods: Depth-First Search (DFS), Breadth-First Search (BFS), and A* Search, which can be used to make intelligent decisions when the initial state, end state, and possible actions are known. Random solutions or greedy solutions can be found for such problems. But these are not optimal in either space or time and efficient approaches in time and space will be explored. We will also understand how to formulate a problem, which involves looking at it and identifying its initial state, goal state, and the actions that are possible in each state. We also need to understand the data structures involved while implementing these search algorithms as they form the basis of search exploration. Finally, we will look into what a heuristic is as this decides the quality of one sub-solution over another and helps you decide which step to take. What you will learn Understand the instances where searches can be used Understand the algorithms that can be used to make decisions more intelligent Formulate a problem by specifying its initial state, goal state, and actions Translate the concepts of the selected search algorithm into code Compare how basic search algorithms will perform for the application Implement algorithmic programming using code examples Who this book is for This book is for developers who are keen to get started with Artificial Intelligence and develop practical AI-based applications. Those developers who want to upgrade their normal applications to smart and intelligent versions will find this book useful. A basic knowledge and understanding of Python are assumed.
Perform cloud-based machine learning and deep learning using Amazon Web Services such as SageMaker, Lex, Comprehend, Translate, and Polly Key Features Explore popular machine learning and deep learning services with their underlying algorithms Discover readily available artificial intelligence(AI) APIs on AWS like Vision and Language Services Design robust architectures to enable experimentation, extensibility, and maintainability of AI apps Book Description From data wrangling through to translating text, you can accomplish this and more with the artificial intelligence and machine learning services available on AWS. With this book, you'll work through hands-on exercises and learn to use these services to solve real-world problems. You'll even design, develop, monitor, and maintain machine and deep learning models on AWS. The book starts with an introduction to AI and its applications in different industries, along with an overview of AWS artificial intelligence and machine learning services. You'll then get to grips with detecting and translating text with Amazon Rekognition and Amazon Translate. The book will assist you in performing speech-to-text with Amazon Transcribe and Amazon Polly. Later, you'll discover the use of Amazon Comprehend for extracting information from text, and Amazon Lex for building voice chatbots. You will also understand the key capabilities of Amazon SageMaker such as wrangling big data, discovering topics in text collections, and classifying images. Finally, you'll cover sales forecasting with deep learning and autoregression, before exploring the importance of a feedback loop in machine learning. By the end of this book, you will have the skills you need to implement AI in AWS through hands-on exercises that cover all aspects of the ML model life cycle. What you will learn Gain useful insights into different machine and deep learning models Build and deploy robust deep learning systems to production Train machine and deep learning models with diverse infrastructure specifications Scale AI apps without dealing with the complexity of managing the underlying infrastructure Monitor and Manage AI experiments efficiently Create AI apps using AWS pre-trained AI services Who this book is for This book is for data scientists, machine learning developers, deep learning researchers, and artificial intelligence enthusiasts who want to harness the power of AWS to implement powerful artificial intelligence solutions. A basic understanding of machine learning concepts is expected.