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Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examples

Key FeaturesAI-based examples to guide you in designing and implementing machine intelligence Build machine intelligence from scratch using artificial intelligence examples Develop machine intelligence from scratch using real artificial intelligenceBook Description

AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples.

This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs).

This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing.

By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions.

What you will learnApply k-nearest neighbors (KNN) to language translations and explore the opportunities in Google Translate Understand chained algorithms combining unsupervised learning with decision trees Solve the XOR problem with feedforward neural networks (FNN) and build its architecture to represent a data flow graph Learn about meta learning models with hybrid neural networks Create a chatbot and optimize its emotional intelligence deficiencies with tools such as Small Talk and data logging Building conversational user interfaces (CUI) for chatbots Writing genetic algorithms that optimize deep learning neural networks Build quantum computing circuitsWho this book is for

Developers and those interested in AI, who want to understand the fundamentals of Artificial Intelligence and implement them practically. Prior experience with Python programming and statistical knowledge is essential to make the most out of this book.

Table of ContentsGetting Started with Next-Generation Artificial Intelligence through Reinforcement LearningBuilding a Reward Matrix Designing Your DatasetsMachine Intelligence Evaluation Functions and Numerical ConvergenceOptimizing Your Solutions with K-Means ClusteringHow to Use Decision Trees to Enhance K-Means ClusteringInnovating AI with Google TranslateOptimizing Blockchains with Naive BayesSolving the XOR Problem with a FNNAbstract Image Classification with CNNConceptual Representation LearningCombining RL and DLAI and the IoTVisualizing Networks with TensorFlow 2.x and TensorBoardPreparing the Input of Chatbots with RBMs and PCASetting Up a Cognitive NLP UI/CUI ChatbotImproving the Emotional Intelligence Deficiencies of ChatbotsGenetic Algorithms in Hybrid Neural NetworksNeuromorphic ComputingQuantum Computing

From the Publisher

How would you describe AI, true AI (also known as AGI), and Strong AI in today’s scenario?

Artificial Intelligence is constantly evolving and has the potential to replicate humans in every field. It gets your system to think smart and learn intelligently.

Artificial General Intelligence (AGI) is not a proven scientific reality to this day. Machine learning and deep learning models are still trained on specific datasets to obtain pre-defined results. In contrast to strong AI, narrow AI is not intended to execute human cognitive skills, rather, it is limited to the use of software to learn or accomplish certain problem solving or reasoning errands. However, transfer learning and domain learning extend the scope of well-trained models to a certain extent.

Presently, AI is predominantly a branch of applied mathematics. Some models can produce art forms (image, music, etc.) but remain based on mathematical models that preclude the expression of emotions.

What’s new in this second edition of Artificial Intelligence by Example?

Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. It will take you to the cutting edge of AI and beyond with innovations that improve existing solutions.

I have added many new AI, ML and DL models: ensemble algorithms, neuromorphic computing, genetic algorithms, hybrid neural networks driven by genetic algorithms, random forests, and more.

This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing.

What are the key takeaways you want readers to get from this book?

The critical takeaway can be summed up in one sentence: learn what AI is, how to build reliable programs, when to use AI, and where to apply it.

If we talk about the key aspects of AI that my book covers, it encapsulates the theories of machine learning, deep learning, and major AI algorithms. The reader will be able to grasp in detail the different stages of e-commerce including manufacturing, services, warehouses, and delivery. It further equips readers with AI solutions combined with IoT, open-source Python programs, cloud platforms, enhancing chatbots, and quantum computing.

By the time you’ve finished this book, you’ll be able to speak as a no-nonsense AI specialist in any situation with knowledge and productive creativity.

Publisher ‏ : ‎ Packt Publishing; 2nd ed. edition (February 28, 2020)
Language ‏ : ‎ English
Paperback ‏ : ‎ 578 pages
ISBN-10 ‏ : ‎ 1839211539
ISBN-13 ‏ : ‎ 978-1839211539
Item Weight ‏ : ‎ 2.18 pounds
Dimensions ‏ : ‎ 9.25 x 7.5 x 1.19 inches