StartseiteGruppenForumMehrZeitgeist
Web-Site durchsuchen
Diese Seite verwendet Cookies für unsere Dienste, zur Verbesserung unserer Leistungen, für Analytik und (falls Sie nicht eingeloggt sind) für Werbung. Indem Sie LibraryThing nutzen, erklären Sie dass Sie unsere Nutzungsbedingungen und Datenschutzrichtlinie gelesen und verstanden haben. Die Nutzung unserer Webseite und Dienste unterliegt diesen Richtlinien und Geschäftsbedingungen.

Ergebnisse von Google Books

Auf ein Miniaturbild klicken, um zu Google Books zu gelangen.

Lädt ...

Python Reinforcement Learning Projects: Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow

von Sean Saito

MitgliederRezensionenBeliebtheitDurchschnittliche BewertungDiskussionen
2Keine5,380,446KeineKeine
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...… (mehr)
Kürzlich hinzugefügt vonB.Bartimaeus, BiblicalFlood
Keine
Lädt ...

Melde dich bei LibraryThing an um herauszufinden, ob du dieses Buch mögen würdest.

Keine aktuelle Diskussion zu diesem Buch.

Keine Rezensionen
keine Rezensionen | Rezension hinzufügen
Du musst dich einloggen, um "Wissenswertes" zu bearbeiten.
Weitere Hilfe gibt es auf der "Wissenswertes"-Hilfe-Seite.
Gebräuchlichster Titel
Originaltitel
Alternative Titel
Ursprüngliches Erscheinungsdatum
Figuren/Charaktere
Wichtige Schauplätze
Wichtige Ereignisse
Zugehörige Filme
Epigraph (Motto/Zitat)
Widmung
Erste Worte
Zitate
Letzte Worte
Hinweis zur Identitätsklärung
Verlagslektoren
Werbezitate von
Originalsprache
Anerkannter DDC/MDS
Anerkannter LCC

Literaturhinweise zu diesem Werk aus externen Quellen.

Wikipedia auf Englisch

Keine

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...

Keine Bibliotheksbeschreibungen gefunden.

Buchbeschreibung
Zusammenfassung in Haiku-Form

Aktuelle Diskussionen

Keine

Beliebte Umschlagbilder

Gespeicherte Links

Bewertung

Durchschnitt: Keine Bewertungen.

Bist das du?

Werde ein LibraryThing-Autor.

 

Über uns | Kontakt/Impressum | LibraryThing.com | Datenschutz/Nutzungsbedingungen | Hilfe/FAQs | Blog | LT-Shop | APIs | TinyCat | Nachlassbibliotheken | Vorab-Rezensenten | Wissenswertes | 211,912,335 Bücher! | Menüleiste: Immer sichtbar