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Python reinforcement learning projects : eight hands-on projects exploring reinforcement learning algorithms using TensorFlow /
Deploy autonomous agents in business systems using powerful Python libraries and sophisticated reinforcement learning models Key Features Implement Q-learning and Markov models with Python and OpenAI Explore the power of TensorFlow to build self-learning models Eight AI projects to gain confidence i...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing Ltd,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Saito, Sean, |e author. | |
| 245 | 1 | 0 | |a Python reinforcement learning projects : |b eight hands-on projects exploring reinforcement learning algorithms using TensorFlow / |c Sean Saito, Yang Wenzhuo, Rajalingappaa Shanmugamani. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing Ltd, |c 2018. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
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| 588 | 0 | |a Online resource; title from title page (viewed November 2, 2018). | |
| 504 | |a Includes bibliographical references. | ||
| 520 | |a Deploy autonomous agents in business systems using powerful Python libraries and sophisticated reinforcement learning models Key Features Implement Q-learning and Markov models with Python and OpenAI Explore the power of TensorFlow to build self-learning models Eight AI projects to gain confidence in building self-trained applications Book Description Reinforcement learning (RL) is the next big leap in the artificial intelligence domain, given that it is unsupervised, optimized, and fast. Python Reinforcement Learning Projects takes you through various aspects and methodologies of reinforcement learning, with the help of insightful projects. You will learn about core concepts of reinforcement learning, such as Q-learning, Markov models, the Monte-Carlo process, and deep reinforcement learning. As you make your way through the book, you'll work on projects with various datasets, including numerical, text, video, and audio, and will gain experience in gaming, image rocessing, audio processing, and recommendation system projects. You'll explore TensorFlow and OpenAI Gym to implement a deep learning RL agent that can play an Atari game. In addition to this, you will learn how to tune and configure RL algorithms and parameters by building agents for different kinds of games. In the concluding chapters, you'll get to grips with building self-learning models that will not only uncover layers of data but also reason and make decisions. By the end of this book, you will have created eight real-world projects that explore reinforcement learning and will have handson experience with real data and artificial intelligence (AI) problems. What you will learn Train and evaluate neural networks built using TensorFlow for RL Use RL algorithms in Python and TensorFlow to solve CartPole balancing Create deep reinforcement learning algorithms to play Atari games Deploy RL algorithms using OpenAI Universe Develop an agent to chat with humans Implement basic actor-critic algorithms for continuous control Apply advanced deep RL algorithms to games such as Minecraft Autogenerate an image classifier using RL Who this book is for Python Reinforcement Learning Projects is for data analysts, data scientists, and machine learning professionals, who have working knowledge of machine learning techniques and are looking to build better performing, automated, and optimized deep learning models. Individuals who want to work on self-learning model projects will also find this b ... | ||
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| 650 | 2 | |a Artificial Intelligence | |
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| 650 | 6 | |a Apprentissage automatique. | |
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| 700 | 1 | |a Wenzhuo, Yang, |e author. | |
| 700 | 1 | |a Shanmugamani, Rajalingappaa, |e author. | |
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