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Artificial Intelligence and Machine Learning Fundamentals : Develop Real-World Applications Powered by the Latest AI Advances.
Artificial Intelligence and Machine Learning Fundamentals teaches you machine learning and neural networks from the ground up using real-world examples. After you complete this book, you will be excited to revamp your current projects or build new intelligent networks.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 003 | OCoLC | ||
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| 008 | 181222s2018 enk o 000 0 eng d | ||
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| 019 | |a 1079847631 |a 1164098599 | ||
| 020 | |a 9781789809206 | ||
| 020 | |a 1789809207 | ||
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| 049 | |a UAMI | ||
| 100 | 1 | |a Nagy, Zsolt. | |
| 245 | 1 | 0 | |a Artificial Intelligence and Machine Learning Fundamentals : |b Develop Real-World Applications Powered by the Latest AI Advances. |
| 260 | |a Birmingham : |b Packt Publishing Ltd, |c 2018. | ||
| 300 | |a 1 online resource (330 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Intro; Preface; Principles of Artificial Intelligence; Introduction; How does AI Solve Real World Problems?; Diversity of Disciplines; Fields and Applications of Artificial Intelligence; Simulating Intelligence -- The Turing Test; AI Tools and Learning Models; Classification and Prediction; Learning Models; The Role of Python in Artificial Intelligence; Why is Python Dominant in Machine Learning, Data Science, and AI?; Anaconda in Python; Python Libraries for Artificial Intelligence; A Brief Introduction to the NumPy Library; Exercise 1: Matrix Operations Using NumPy; Python for Game AI | |
| 505 | 8 | |a Intelligent Agents in GamesBreadth First Search and Depth First Search; Exploring the State Space of a Game; Exercise 2: Estimating the Number of Possible States in Tic-Tac-Toe Game; Exercise 3: Creating an AI Randomly; Activity 1: Generating All Possible Sequences of Steps in a Tic-Tac-Toe Game; Summary; AI with Search Techniques and Games; Introduction; Exercise 4: Teaching the Agent to Win; Activity 2: Teaching the Agent to Realize Situations When It Defends Against Losses; Activity 3: Fixing the First and Second Moves of the AI to Make it Invincible; Heuristics | |
| 505 | 8 | |a Uninformed and Informed SearchCreating Heuristics; Admissible and Non-Admissible Heuristics; Heuristic Evaluation; Exercise 5: Tic-Tac-Toe Static Evaluation with a Heuristic Function; Using Heuristics for an Informed Search; Types of Heuristics; Pathfinding with the A* Algorithm; Exercise 6: Finding the Shortest Path to Reach a Goal; Exercise 7: Finding the Shortest Path Using BFS; Introducing the A* Algorithm; A* Search in Practice Using the simpleai Library; Game AI with the Minmax Algorithm and Alpha-Beta Pruning; Search Algorithms for Turn-Based Multiplayer Games; The Minmax Algorithm | |
| 505 | 8 | |a Optimizing the Minmax Algorithm with Alpha-Beta PruningDRYing up the Minmax Algorithm -- The NegaMax Algorithm; Using the EasyAI Library; Activity 4: Connect Four; Summary; Regression; Introduction; Linear Regression with One Variable; What Is Regression?; Features and Labels; Feature Scaling; Cross-Validation with Training and Test Data; Fitting a Model on Data with scikit-learn; Linear Regression Using NumPy Arrays; Fitting a Model Using NumPy Polyfit; Predicting Values with Linear Regression; Activity 5: Predicting Population; Linear Regression with Multiple Variables | |
| 505 | 8 | |a Multiple Linear RegressionThe Process of Linear Regression; Importing Data from Data Sources; Loading Stock Prices with Yahoo Finance; Loading Files with pandas; Loading Stock Prices with Quandl; Exercise 8: Using Quandl to Load Stock Prices; Preparing Data for Prediction; Performing and Validating Linear Regression; Predicting the Future; Polynomial and Support Vector Regression; Polynomial Regression with One Variable; Exercise 9: 1st, 2nd, and 3rd Degree Polynomial Regression; Polynomial Regression with Multiple Variables; Support Vector Regression | |
| 500 | |a Support Vector Machines with a 3 Degree Polynomial Kernel | ||
| 520 | |a Artificial Intelligence and Machine Learning Fundamentals teaches you machine learning and neural networks from the ground up using real-world examples. After you complete this book, you will be excited to revamp your current projects or build new intelligent networks. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Programming & scripting languages: general. |2 bicssc | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Computers |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Computers |x Programming Languages |x Python. |2 bisacsh | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
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| 776 | 0 | 8 | |i Print version: |a Nagy, Zsolt. |t Artificial Intelligence and Machine Learning Fundamentals : Develop Real-World Applications Powered by the Latest AI Advances. |d Birmingham : Packt Publishing Ltd, ©2018 |z 9781789801651 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5620491 |z Texto completo |
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