| Titre : |
Artificial intelligence by example : acquire advanced AI, machine learning, and deep learning design skills |
| Type de document : |
texte imprimé |
| Auteurs : |
Denis Rothman, Auteur |
| Mention d'édition : |
2nd ed |
| Editeur : |
Birmingham : Packt Publishing |
| Année de publication : |
2020 |
| Importance : |
XXI, 549 p. |
| Présentation : |
ill. |
| Format : |
24 cm |
| ISBN/ISSN/EAN : |
978-1-83921-153-9 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Artificial intelligence
Machine learning |
| Index. décimale : |
004.8 Intelligence artificielle |
| Résumé : |
This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs).
This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing.
By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions. |
| Note de contenu : |
Summary :
1. Getting started with next-generation artificial intelligence through reinforcement learning.
2. Building a reward matrix designing your datasets.
3. Machine intelligence evaluation functions and numerical convergence.
4. Optimizing your solutions with k-means clustering.
5. How to use decision trees to enhance k-means clustering.
6. Innovating ai with google translate.
7. Optimizing blockchains with naive bayes.
8. Solving the xor problem with a fnn.
9. Abstract image classification with cnn.
10. Conceptual representation learning.
11. Combining rl and dl.
12. Ai and the iot.
13. Visualizing networks with tensorflow 2.x and tensorboard.
14. Preparing the input of chatbots with rbms and pca.
15. Setting up a cognitive nlp ui/cui chatbot
16. Improving the emotional intelligence. deficiencies of chatbots.
17. Genetic algorithms in hybrid neural networks.
18. Neuromorphic computing.
19. Quantum computing |
Artificial intelligence by example : acquire advanced AI, machine learning, and deep learning design skills [texte imprimé] / Denis Rothman, Auteur . - 2nd ed . - Birmingham : Packt Publishing, 2020 . - XXI, 549 p. : ill. ; 24 cm. ISBN : 978-1-83921-153-9 Langues : Anglais ( eng)
| Mots-clés : |
Artificial intelligence
Machine learning |
| Index. décimale : |
004.8 Intelligence artificielle |
| Résumé : |
This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs).
This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing.
By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions. |
| Note de contenu : |
Summary :
1. Getting started with next-generation artificial intelligence through reinforcement learning.
2. Building a reward matrix designing your datasets.
3. Machine intelligence evaluation functions and numerical convergence.
4. Optimizing your solutions with k-means clustering.
5. How to use decision trees to enhance k-means clustering.
6. Innovating ai with google translate.
7. Optimizing blockchains with naive bayes.
8. Solving the xor problem with a fnn.
9. Abstract image classification with cnn.
10. Conceptual representation learning.
11. Combining rl and dl.
12. Ai and the iot.
13. Visualizing networks with tensorflow 2.x and tensorboard.
14. Preparing the input of chatbots with rbms and pca.
15. Setting up a cognitive nlp ui/cui chatbot
16. Improving the emotional intelligence. deficiencies of chatbots.
17. Genetic algorithms in hybrid neural networks.
18. Neuromorphic computing.
19. Quantum computing |
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