English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 7h 28m | 2.52 GB

From start to finish, the best book to help you learn AI algorithms and recall why and how you use them.

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.

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.

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

## Table of Contents

1 Preface – Our obsession with technology and automation

2 Preface – Ethics, legal matters, and our responsibility

3 Intuition of artificial intelligence

4 A brief history of artificial intelligence

5 Super intelligence – The great unknown

6 Banking – Fraud detection

7 Search fundamentals

8 Representing state – Creating a framework to represent problem spaces and solutions

9 Breadth-first search – Looking wide before looking deep

10 Depth-first search – Looking deep before looking wide

11 Intelligent search

12 A search

13 Use cases for informed search algorithms

14 Exercise – What values would propagate in the following Min-max tree

15 Alpha-beta pruning – Optimize by exploring the sensible paths only

16 Evolutionary algorithms

17 Problems applicable to evolutionary algorithms

18 Encoding the solution spaces

19 Selecting parents based on their fitness

20 Two-point crossover – Inheriting more parts from each parent

21 Configuring the parameters of a genetic algorithm

22 Advanced evolutionary approaches

23 Arithmetic crossover – Reproduce with math

24 Change node mutation – Changing the value of a node

25 Swarm intelligence – Ants

26 Representing state – What do paths and ants look like

27 Set up the population of ants

28 Updating pheromones based on ant tours

29 Swarm intelligence – Particles

30 Problems applicable to particle swarm optimization

31 Calculate the fitness of each particle

32 Position update

33 Machine learning

34 Collecting and understanding data – Know your context

35 Ambiguous values

36 Finding the mean of the features

37 Testing the model – Determine the accuracy of the model

38 Classification with decision trees

39 Decision-tree learning life cycle

40 Classifying examples with decision trees

41 Artificial neural networks

42 Exercise – Calculate the output of the following input for the Perceptron

43 Forward propagation – Using a trained ANN

44 Backpropagation – Training an ANN

45 Options for activation functions

46 Bias

47 Reinforcement learning with Q-learning

48 Problems applicable to reinforcement learning

49 Training with the simulation using Q-learning

50 Exercise – Calculate the change in values for the Q-table

51 Deep learning approaches to reinforcement learning

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