Video description
In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.
From start to finish, the best book to help you learn AI algorithms and recall why and how you use them.
Linda Ristevski, York Region District School Board
Grokking Artificial Intelligence Algorithms is a fully-illustrated and interactive tutorial guide to the different approaches and algorithms that underpin AI. Written in simple language and with lots of visual references and hands-on examples, you'll learn the concepts, terminology, and theory you need to effectively incorporate AI algorithms into your applications. And to make sure you truly grok as you go, you'll use each algorithm in practice with creative coding exercises—including building a maze puzzle game, performing diamond data analysis, and even exploring drone material optimization.
about the technology
Artificial intelligence touches every part of our lives. It powers our shopping and TV recommendations; it informs our medical diagnoses. Embracing this new world means mastering the core algorithms at the heart of AI.
about the book
Grokking Artificial Intelligence Algorithms uses illustrations, exercises, and jargon-free explanations to teach fundamental AI concepts. All you need is the algebra you remember from high school math class. Explore coding challenges like detecting bank fraud, creating artistic masterpieces, and setting a self-driving car in motion.
what's inside
- Use cases for different AI algorithms
- Intelligent search for decision making
- Biologically inspired algorithms
- Machine learning and neural networks
- Reinforcement learning to build a better robot
about the audience
For software developers with high school–level algebra and calculus skills.
about the author
Rishal Hurbans is a technologist, founder, and international speaker.
This book takes an impossibly broad area of computer science and communicates what working developers need to understand in a clear and thorough way.David Jacobs, Product Advance Local
The most comprehensive content I have seen on AI algorithms.
Karan Nih, Classic Software Solutions
This book removes the fear of stepping into the mechanics of AI.
Kyle Peterson, University of Iowa Athletics
NARRATED BY JEROMY LLOYD AND JULIE BRIERLEY
Table of contents
- Preface 1. Our obsession with technology and automation
- Preface 2. Ethics, legal matters, and our responsibility
- Chapter 1. Intuition of artificial intelligence
- Chapter 1. A brief history of artificial intelligence
- Chapter 1. Super intelligence: The great unknown
- Chapter 1. Banking: Fraud detection
- Chapter 2. Search fundamentals
- Chapter 2. Representing state: Creating a framework to represent problem spaces and solutions
- Chapter 2. Breadth-first search: Looking wide before looking deep
- Chapter 2. Depth-first search: Looking deep before looking wide
- Chapter 3. Intelligent search
- Chapter 2. A* search
- Chapter 3. Use cases for informed search algorithms
- Chapter 3. Exercise: What values would propagate in the following Min-max tree?
- Chapter 3. Alpha-beta pruning: Optimize by exploring the sensible paths only
- Chapter 4. Evolutionary algorithms
- Chapter 4. Problems applicable to evolutionary algorithms
- Chapter 4. Encoding the solution spaces
- Chapter 4. Selecting parents based on their fitness
- Chapter 4. Two-point crossover: Inheriting more parts from each parent
- Chapter 4. Configuring the parameters of a genetic algorithm
- Chapter 5. Advanced evolutionary approaches
- Chapter 5. Arithmetic crossover: Reproduce with math
- Chapter 5. Change node mutation: Changing the value of a node
- Chapter 6. Swarm intelligence: Ants
- Chapter 6. Representing state: What do paths and ants look like?
- Chapter 6. Set up the population of ants
- Chapter 6. Updating pheromones based on ant tours
- Chapter 7. Swarm intelligence: Particles
- Chapter 7. Problems applicable to particle swarm optimization
- Chapter 7. Calculate the fitness of each particle
- Chapter 7. Position update
- Chapter 8. Machine learning
- Chapter 8. Collecting and understanding data: Know your context
- Chapter 8. Ambiguous values
- Chapter 8. Finding the mean of the features
- Chapter 8. Testing the model: Determine the accuracy of the model
- Chapter 8. Classification with decision trees
- Chapter 8. Decision-tree learning life cycle
- Chapter 8. Classifying examples with decision trees
- Chapter 9. Artificial neural networks
- Chapter 9. Exercise: Calculate the output of the following input for the Perceptron
- Chapter 9. Forward propagation: Using a trained ANN
- Chapter 9. Backpropagation: Training an ANN
- Chapter 9. Options for activation functions
- Chapter 9. Bias
- Chapter 10. Reinforcement learning with Q-learning
- Chapter 10. Problems applicable to reinforcement learning
- Chapter 10. Training with the simulation using Q-learning
- Chapter 10. Exercise: Calculate the change in values for the Q-table
- Chapter 10. Deep learning approaches to reinforcement learning
Product information
- Title: Grokking Artificial Intelligence Algorithms, Video Edition
- Author(s):
- Release date: July 2020
- Publisher(s): Manning Publications
- ISBN: 9781617296185VE
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