Hands-On Artificial Intelligence for Beginners
An introduction to AI concepts, algorithms, and their implementation
Patrick D. Smith
- 362 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Hands-On Artificial Intelligence for Beginners
An introduction to AI concepts, algorithms, and their implementation
Patrick D. Smith
About This Book
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.
Frequently asked questions
Information
Deep Learning for Finance
- Introduction to deep learning in finance
- Deep learning in trading
- Deep learning in asset management
Requirements
Introduction to AI in finance
- Deal-based firms: Investment banking, venture capital, and private equity
- Public markets: Hedge funds, trading departments at large banks, and various asset management firms
Deep learning in trading
- Fundamental analysis looks at the underlying factors that could influence a financial derivative, such as the general financial health of a company
- Technical analysis looks at the actual performance of the financial derivative in a more mathematical sense, attempting to predict price movements based on patterns in the asset's price movements
- Buy-side firms: Firms utilize algorithmic trading to manage their mid-to long-term portfolio investments
- Sell-side firms: Firms use high-frequency algorithmic trading to take advantage of market opportunities and move markets themselves
- Systematic traders: These individuals and firms try to match a long-term investment with a short-term investment of highly correlated financial derivatives