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Grokking Artificial Intelligence Algorithms [Pehme köide]

4.03/5 (145 hinnangut Goodreads-ist)
  • Formaat: Paperback / softback, 392 pages, kõrgus x laius x paksus: 234x188x23 mm, kaal: 740 g, Illustrations
  • Ilmumisaeg: 24-Aug-2020
  • Kirjastus: Manning Publications
  • ISBN-10: 161729618X
  • ISBN-13: 9781617296185
Teised raamatud teemal:
Grokking Artificial Intelligence AlgorithmsSuurem pilt
  • Formaat: Paperback / softback, 392 pages, kõrgus x laius x paksus: 234x188x23 mm, kaal: 740 g, Illustrations
  • Ilmumisaeg: 24-Aug-2020
  • Kirjastus: Manning Publications
  • ISBN-10: 161729618X
  • ISBN-13: 9781617296185
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781617296185.html
Märksõnad:

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, readers learn the concepts, terminology, and theory they need to effectively incorporate AI algorithms into their applications.

Grokking Artificial Intelligence Algorithms uses simple language, jargon-busting explanations, and hand-drawn diagrams to open up complex algorithms. Don't worry if you aren't a calculus wunderkind; you'll need only the algebra you picked up in math class.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.



&;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

Key Features
Master the core algorithms of deep learning and AI
Build an intuitive understanding of AI problems and solutions
Written in simple language, with lots of illustrations and hands-on examples
Creative coding exercises, including building a maze puzzle game and exploring drone optimization

About The Book

&;Artificial intelligence&; requires teaching a computer how to approach different types of problems in a systematic way. The core of AI is the algorithms that the system uses to do things like identifying objects in an image, interpreting the meaning of text, or looking for patterns in data to spot fraud and other anomalies.  Mastering the core algorithms for search, image recognition, and other common tasks is essential to building good AI applications

Grokking Artificial Intelligence Algorithms uses illustrations, exercises, and jargon-free explanations to teach fundamental AI concepts.You&;ll explore coding challenges like detect­ing bank fraud, creating artistic masterpieces, and setting a self-driving car in motion. All you need is the algebra you remember from high school math class and beginning programming skills. 

What You Will Learn

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

This Book Is Written For
For software developers with high school&;level math skills.

About the Author
Rishal Hurbans is a technologist, startup and AI group founder, and international speaker.

Table of Contents

1 Intuition of artificial intelligence
2 Search fundamentals
3 Intelligent search
4 Evolutionary algorithms
5 Advanced evolutionary approaches
6 Swarm intelligence: Ants
7 Swarm intelligence: Particles
8 Machine learning
9 Artificial neural networks
10 Reinforcement learning with Q-learning
Preface ix
Acknowledgments xvii
About This Book xix
About The Author xxiii
1 Intuition Of Artificial Intelligence
1(20)
What is artificial intelligence?
1(5)
A brief history of artificial intelligence
6(2)
Problem types and problem-solving paradigms
8(2)
Intuition of artificial intelligence concepts
10(4)
Uses for artificial intelligence algorithms
14(7)
2 Search Fundamentals
21(38)
What are planning and searching?
21(3)
Cost of computation: The reason for smart algorithms
24(1)
Problems applicable to searching algorithms
25(3)
Representing state: Creating a framework to represent problem spaces and solutions
28(6)
Uninformed search: Looking blindly for solutions
34(2)
Breadth-first search: Looking wide before looking deep
36(9)
Depth-first search: Looking deep before looking wide
45(8)
Use cases for uninformed search algorithms
53(1)
Optional: More about graph categories
54(2)
Optional: More ways to represent graphs
56(3)
3 Intelligent Search
59(32)
Defining heuristics: Designing educated guesses
59(4)
Informed search: Looking for solutions with guidance
63(9)
Adversarial search: Looking for solutions in a changing environment
72(19)
4 Evolutionary Algorithms
91(40)
What is evolution?
91(4)
Problems applicable to evolutionary algorithms
95(4)
Genetic algorithm: Life cycle
99(3)
Encoding the solution spaces
102(6)
Creating a population of solutions
108(2)
Measuring fitness of individuals in a population
110(2)
Selecting parents based on their fitness
112(4)
Reproducing individuals from parents
116(6)
Populating the next generation
122(4)
Configuring the parameters of a genetic algorithm
126(1)
Use cases for evolutionary algorithms
127(4)
5 Advanced Evolutionary Approaches
131(22)
Evolutionary algorithm life cycle
131(2)
Alternative selection strategies
133(4)
Real-value encoding: Working with real numbers
137(4)
Order encoding: Working with sequences
141(3)
Tree encoding: Working with hierarchies
144(4)
Common types of evolutionary algorithms
148(1)
Glossary of evolutionary algorithm terms
149(1)
More use cases for evolutionary algorithms
150(3)
6 Swarm Intelligence: Ants
153(36)
What is swarm intelligence?
153(3)
Problems applicable to ant colony optimization
156(4)
Representing state: What do paths and ants look like?
160(4)
The ant colony optimization algorithm life cycle
164(23)
Use cases for ant colony optimization algorithms
187(2)
7 Swarm Intelligence: Particles
189(38)
What is particle swarm optimization?
189(3)
Optimization problems: A slightly more technical perspective
192(3)
Problems applicable to particle swarm optimization
195(3)
Representing state: What do particles look like?
198(1)
Particle swarm optimization life cycle
199(24)
Use cases for particle swarm optimization algorithms
223(4)
8 Machine Learning
227(52)
What is machine learning?
227(3)
Problems applicable to machine learning
230(2)
A machine learning workflow
232(24)
Classification with decision trees
256(19)
Other popular machine learning algorithms
275(1)
Use cases for machine learning algorithms
276(3)
9 Artificial Neural Networks
279(44)
What are artificial neural networks?
280(3)
The Perceptron: A representation of a neuron
283(4)
Defining artificial neural networks
287(8)
Forward propagation: Using a trained ANN
295(8)
Backpropagation: Training an ANN
303(11)
Options for activation functions
314(2)
Designing artificial neural networks
316(3)
Artificial neural network types and use cases
319(4)
10 Reinforcement Learning With Q-Learning
323(32)
What is reinforcement learning?
323(4)
Problems applicable to reinforcement learning
327(2)
The life cycle of reinforcement learning
329(20)
Deep learning approaches to reinforcement learning
349(1)
Use cases for reinforcement learning
350(5)
Index 355
Rishal Hurbans is a solutions architect for one of the largest technology companies in South Africa, the founder of the Artificial Intelligence South Africa group, and an Intel Innovator in the AI space.