Keras reinforcement learning projects: 9 projects exploring popular reinforcement learning techniques to build self-learning agents
Keras Reinforcement Learning Projects book teaches you essential concept, techniques and, models of reinforcement learning using best real-world demonstrations. You will explore popular algorithms such as Markov decision process, Monte Carlo, Q-learning making you equipped with complex statistics in...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Birmingham ; Mumbai
Packt Publishing
2018
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| Schlagworte: | |
| Zusammenfassung: | Keras Reinforcement Learning Projects book teaches you essential concept, techniques and, models of reinforcement learning using best real-world demonstrations. You will explore popular algorithms such as Markov decision process, Monte Carlo, Q-learning making you equipped with complex statistics in various projects with the help of Keras Intro -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Overview of Keras Reinforcement Learning -- Basic concepts of machine learning -- Discovering the different types of machine learning -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Building machine learning models step by step -- Getting started with reinforcement learning -- Agent-environment interface -- Markov Decision Process -- Discounted cumulative reward -- Exploration versus exploitation -- Reinforcement learning algorithms -- Dynamic Programming -- Monte Carlo methods -- Temporal difference learning -- SARSA -- Q-learning -- Deep Q-learning -- Summary -- Chapter 2: Simulating Random Walks -- Random walks -- One-dimensional random walk -- Simulating 1D random walk -- Markov chains -- Stochastic process -- Probability calculation -- Markov chain definition -- Transition matrix -- Transition diagram -- Weather forecasting with Markov chains -- Generating pseudorandom text with Markov chains -- Summary -- Chapter 3: Optimal Portfolio Selection -- Dynamic Programming -- Divide and conquer versus Dynamic Programming -- Memoization -- Dynamic Programming in reinforcement-learning applications -- Optimizing a financial portfolio -- Optimization techniques -- Solving the knapsack problem using Dynamic Programming -- Different approaches to the problem -- Brute force -- Greedy algorithms -- Dynamic Programming -- Summary -- Chapter 4: Forecasting Stock Market Prices -- Monte Carlo methods -- Historical background -- Basic concepts of the Monte Carlo simulation -- Monte Carlo applications -- Numerical integration using the Monte Carlo method -- Monte Carlo for prediction and control -- Amazon stock price prediction using Python -- Exploratory analysis -- The Geometric Brownian motion model Monte Carlo simulation -- Summary -- Chapter 5: Delivery Vehicle Routing Application -- Temporal difference learning -- SARSA -- Q-learning -- Basics of graph theory -- The adjacency matrix -- Adjacency lists -- Graphs as data structures in Python -- Graphs using the NetworkX package -- Finding the shortest path -- The Dijkstra algorithm -- The Dijkstra algorithm using the NetworkX package -- The Google Maps algorithm -- The Vehicle Routing Problem -- Summary -- Chapter 6: Continuous Balancing of a Rotating Mechanical System -- Neural network basic concepts -- The Keras neural network model -- Classifying breast cancer using the neural network -- Deep reinforcement learning -- The Keras-RL package -- Continuous control with deep reinforcement learning -- Summary -- Chapter 7: Dynamic Modeling of a Segway as an Inverted Pendulum System -- How Segways work -- System modeling basics -- OpenAI Gym -- OpenAI Gym methods -- OpenAI Gym installation -- The CartPole system -- Q-learning solution -- Deep Q-learning solution -- Summary -- Chapter 8: Robot Control System Using Deep Reinforcement Learning -- Robot control -- Robotics overview -- Robot evolution -- First-generation robots -- Second-generation robots -- Third-generation robots -- Fourth-generation robots -- Robot autonomy -- Robot mobility -- Automatic control -- Control architectures -- The FrozenLake environment -- The Q-learning solution -- A Deep Q-learning solution -- Summary -- Chapter 9: Handwritten Digit Recognizer -- Handwritten digit recognition -- Optical Character Recognition -- Computer vision -- Handwritten digit recognition using an autoencoder -- Loading data -- Model architecture -- Deep autoencoder Q-learning -- Summary -- Chapter 10: Playing the Board Game Go -- Game theory -- Basic concepts -- Game types -- Cooperative games -- Symmetrical games -- Zero-sum games |
| Beschreibung: | IV, 277 Seiten Illustrationen |
| ISBN: | 9781789342093 |
Internformat
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| 520 | 3 | |a Keras Reinforcement Learning Projects book teaches you essential concept, techniques and, models of reinforcement learning using best real-world demonstrations. You will explore popular algorithms such as Markov decision process, Monte Carlo, Q-learning making you equipped with complex statistics in various projects with the help of Keras | |
| 520 | 3 | |a Intro -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Overview of Keras Reinforcement Learning -- Basic concepts of machine learning -- Discovering the different types of machine learning -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Building machine learning models step by step -- Getting started with reinforcement learning -- Agent-environment interface -- Markov Decision Process -- Discounted cumulative reward -- Exploration versus exploitation -- Reinforcement learning algorithms -- Dynamic Programming -- Monte Carlo methods -- Temporal difference learning -- SARSA -- Q-learning -- Deep Q-learning -- Summary -- Chapter 2: Simulating Random Walks -- Random walks -- One-dimensional random walk -- Simulating 1D random walk -- Markov chains -- Stochastic process -- Probability calculation -- Markov chain definition -- Transition matrix -- Transition diagram -- Weather forecasting with Markov chains -- Generating pseudorandom text with Markov chains -- Summary -- Chapter 3: Optimal Portfolio Selection -- Dynamic Programming -- Divide and conquer versus Dynamic Programming -- Memoization -- Dynamic Programming in reinforcement-learning applications -- Optimizing a financial portfolio -- Optimization techniques -- Solving the knapsack problem using Dynamic Programming -- Different approaches to the problem -- Brute force -- Greedy algorithms -- Dynamic Programming -- Summary -- Chapter 4: Forecasting Stock Market Prices -- Monte Carlo methods -- Historical background -- Basic concepts of the Monte Carlo simulation -- Monte Carlo applications -- Numerical integration using the Monte Carlo method -- Monte Carlo for prediction and control -- Amazon stock price prediction using Python -- Exploratory analysis -- The Geometric Brownian motion model | |
| 520 | 3 | |a Monte Carlo simulation -- Summary -- Chapter 5: Delivery Vehicle Routing Application -- Temporal difference learning -- SARSA -- Q-learning -- Basics of graph theory -- The adjacency matrix -- Adjacency lists -- Graphs as data structures in Python -- Graphs using the NetworkX package -- Finding the shortest path -- The Dijkstra algorithm -- The Dijkstra algorithm using the NetworkX package -- The Google Maps algorithm -- The Vehicle Routing Problem -- Summary -- Chapter 6: Continuous Balancing of a Rotating Mechanical System -- Neural network basic concepts -- The Keras neural network model -- Classifying breast cancer using the neural network -- Deep reinforcement learning -- The Keras-RL package -- Continuous control with deep reinforcement learning -- Summary -- Chapter 7: Dynamic Modeling of a Segway as an Inverted Pendulum System -- How Segways work -- System modeling basics -- OpenAI Gym -- OpenAI Gym methods -- OpenAI Gym installation -- The CartPole system -- Q-learning solution -- Deep Q-learning solution -- Summary -- Chapter 8: Robot Control System Using Deep Reinforcement Learning -- Robot control -- Robotics overview -- Robot evolution -- First-generation robots -- Second-generation robots -- Third-generation robots -- Fourth-generation robots -- Robot autonomy -- Robot mobility -- Automatic control -- Control architectures -- The FrozenLake environment -- The Q-learning solution -- A Deep Q-learning solution -- Summary -- Chapter 9: Handwritten Digit Recognizer -- Handwritten digit recognition -- Optical Character Recognition -- Computer vision -- Handwritten digit recognition using an autoencoder -- Loading data -- Model architecture -- Deep autoencoder Q-learning -- Summary -- Chapter 10: Playing the Board Game Go -- Game theory -- Basic concepts -- Game types -- Cooperative games -- Symmetrical games -- Zero-sum games | |
| 653 | 0 | |a Machine learning | |
| 653 | 0 | |a Neural networks | |
| 653 | 0 | |a Machine learning | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-031546139 | ||
Datensatz im Suchindex
| _version_ | 1804180520380137472 |
|---|---|
| any_adam_object | |
| author | Ciaburro, Giuseppe |
| author_GND | (DE-588)1158671741 |
| author_facet | Ciaburro, Giuseppe |
| author_role | aut |
| author_sort | Ciaburro, Giuseppe |
| author_variant | g c gc |
| building | Verbundindex |
| bvnumber | BV046166294 |
| classification_rvk | ST 300 |
| ctrlnum | (OCoLC)1126556236 (DE-599)BVBBV046166294 |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV046166294 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T08:37:04Z |
| institution | BVB |
| isbn | 9781789342093 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031546139 |
| oclc_num | 1126556236 |
| open_access_boolean | |
| owner | DE-11 |
| owner_facet | DE-11 |
| physical | IV, 277 Seiten Illustrationen |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Packt Publishing |
| record_format | marc |
| spelling | Ciaburro, Giuseppe Verfasser (DE-588)1158671741 aut Keras reinforcement learning projects 9 projects exploring popular reinforcement learning techniques to build self-learning agents Giuseppe Ciaburro Birmingham ; Mumbai Packt Publishing 2018 © 2018 IV, 277 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Keras Reinforcement Learning Projects book teaches you essential concept, techniques and, models of reinforcement learning using best real-world demonstrations. You will explore popular algorithms such as Markov decision process, Monte Carlo, Q-learning making you equipped with complex statistics in various projects with the help of Keras Intro -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Overview of Keras Reinforcement Learning -- Basic concepts of machine learning -- Discovering the different types of machine learning -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Building machine learning models step by step -- Getting started with reinforcement learning -- Agent-environment interface -- Markov Decision Process -- Discounted cumulative reward -- Exploration versus exploitation -- Reinforcement learning algorithms -- Dynamic Programming -- Monte Carlo methods -- Temporal difference learning -- SARSA -- Q-learning -- Deep Q-learning -- Summary -- Chapter 2: Simulating Random Walks -- Random walks -- One-dimensional random walk -- Simulating 1D random walk -- Markov chains -- Stochastic process -- Probability calculation -- Markov chain definition -- Transition matrix -- Transition diagram -- Weather forecasting with Markov chains -- Generating pseudorandom text with Markov chains -- Summary -- Chapter 3: Optimal Portfolio Selection -- Dynamic Programming -- Divide and conquer versus Dynamic Programming -- Memoization -- Dynamic Programming in reinforcement-learning applications -- Optimizing a financial portfolio -- Optimization techniques -- Solving the knapsack problem using Dynamic Programming -- Different approaches to the problem -- Brute force -- Greedy algorithms -- Dynamic Programming -- Summary -- Chapter 4: Forecasting Stock Market Prices -- Monte Carlo methods -- Historical background -- Basic concepts of the Monte Carlo simulation -- Monte Carlo applications -- Numerical integration using the Monte Carlo method -- Monte Carlo for prediction and control -- Amazon stock price prediction using Python -- Exploratory analysis -- The Geometric Brownian motion model Monte Carlo simulation -- Summary -- Chapter 5: Delivery Vehicle Routing Application -- Temporal difference learning -- SARSA -- Q-learning -- Basics of graph theory -- The adjacency matrix -- Adjacency lists -- Graphs as data structures in Python -- Graphs using the NetworkX package -- Finding the shortest path -- The Dijkstra algorithm -- The Dijkstra algorithm using the NetworkX package -- The Google Maps algorithm -- The Vehicle Routing Problem -- Summary -- Chapter 6: Continuous Balancing of a Rotating Mechanical System -- Neural network basic concepts -- The Keras neural network model -- Classifying breast cancer using the neural network -- Deep reinforcement learning -- The Keras-RL package -- Continuous control with deep reinforcement learning -- Summary -- Chapter 7: Dynamic Modeling of a Segway as an Inverted Pendulum System -- How Segways work -- System modeling basics -- OpenAI Gym -- OpenAI Gym methods -- OpenAI Gym installation -- The CartPole system -- Q-learning solution -- Deep Q-learning solution -- Summary -- Chapter 8: Robot Control System Using Deep Reinforcement Learning -- Robot control -- Robotics overview -- Robot evolution -- First-generation robots -- Second-generation robots -- Third-generation robots -- Fourth-generation robots -- Robot autonomy -- Robot mobility -- Automatic control -- Control architectures -- The FrozenLake environment -- The Q-learning solution -- A Deep Q-learning solution -- Summary -- Chapter 9: Handwritten Digit Recognizer -- Handwritten digit recognition -- Optical Character Recognition -- Computer vision -- Handwritten digit recognition using an autoencoder -- Loading data -- Model architecture -- Deep autoencoder Q-learning -- Summary -- Chapter 10: Playing the Board Game Go -- Game theory -- Basic concepts -- Game types -- Cooperative games -- Symmetrical games -- Zero-sum games Machine learning Neural networks |
| spellingShingle | Ciaburro, Giuseppe Keras reinforcement learning projects 9 projects exploring popular reinforcement learning techniques to build self-learning agents |
| title | Keras reinforcement learning projects 9 projects exploring popular reinforcement learning techniques to build self-learning agents |
| title_auth | Keras reinforcement learning projects 9 projects exploring popular reinforcement learning techniques to build self-learning agents |
| title_exact_search | Keras reinforcement learning projects 9 projects exploring popular reinforcement learning techniques to build self-learning agents |
| title_full | Keras reinforcement learning projects 9 projects exploring popular reinforcement learning techniques to build self-learning agents Giuseppe Ciaburro |
| title_fullStr | Keras reinforcement learning projects 9 projects exploring popular reinforcement learning techniques to build self-learning agents Giuseppe Ciaburro |
| title_full_unstemmed | Keras reinforcement learning projects 9 projects exploring popular reinforcement learning techniques to build self-learning agents Giuseppe Ciaburro |
| title_short | Keras reinforcement learning projects |
| title_sort | keras reinforcement learning projects 9 projects exploring popular reinforcement learning techniques to build self learning agents |
| title_sub | 9 projects exploring popular reinforcement learning techniques to build self-learning agents |
| work_keys_str_mv | AT ciaburrogiuseppe kerasreinforcementlearningprojects9projectsexploringpopularreinforcementlearningtechniquestobuildselflearningagents |