Hands-On Artificial Intelligence for Beginners: An introduction to AI concepts, algorithms, and their implementation

Hands-On Artificial Intelligence for Beginners: An introduction to AI concepts, algorithms, and their implementation
Hands-On Artificial Intelligence for Beginners: An introduction to AI concepts, algorithms, and their implementation by D. Smith, Patrick
English | 2019 | ISBN: 1788991063 | 362 Pages | EPUB | 15 MB

Grasp the fundamentals of Artificial Intelligence and build your own intelligent systems with ease
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