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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