Chiudi

Aggiungi l'articolo in

Chiudi
Aggiunto

L’articolo è stato aggiunto alla lista dei desideri

Chiudi

Crea nuova lista

Dati e Statistiche
Wishlist Salvato in 0 liste dei desideri
Practical Deep Reinforcement Learning with Python: Concise Implementation of Algorithms, Simplified Maths, and Effective Use of TensorFlow and PyTorch (English Edition)
Scaricabile subito
13,99 €
13,99 €
Scaricabile subito
Chiudi
Altri venditori
Prezzo e spese di spedizione
ibs
13,99 € Spedizione gratuita
scaricabile subito scaricabile subito
Info
Nuovo
Altri venditori
Prezzo e spese di spedizione
ibs
13,99 € Spedizione gratuita
scaricabile subito scaricabile subito
Info
Nuovo
Altri venditori
Prezzo e spese di spedizione
Chiudi

Tutti i formati ed edizioni

Chiudi
Practical Deep Reinforcement Learning with Python: Concise Implementation of Algorithms, Simplified Maths, and Effective Use of TensorFlow and PyTorch (English Edition)
Chiudi

Promo attive (0)

Chiudi
Practical Deep Reinforcement Learning with Python: Concise Implementation of Algorithms, Simplified Maths, and Effective Use of TensorFlow and PyTorch (English Edition)
Chiudi

Informazioni del regalo

Descrizione


Introducing Practical Smart Agents Development using Python, PyTorch, and TensorFlow KEY FEATURES ? Exposure to well-known RL techniques, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical. ? Hands-on experience with TensorFlow and PyTorch on Reinforcement Learning projects. ? Everything is concise, up-to-date, and visually explained with simplified mathematics. DESCRIPTION Reinforcement learning is a fascinating branch of AI that differs from standard machine learning in several ways. Adaptation and learning in an unpredictable environment is the part of this project. There are numerous real-world applications for reinforcement learning these days, including medical, gambling, human imitation activity, and robotics. This book introduces readers to reinforcement learning from a pragmatic point of view. The book does involve mathematics, but it does not attempt to overburden the reader, who is a beginner in the field of reinforcement learning. The book brings a lot of innovative methods to the reader's attention in much practical learning, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical methods. While you understand these techniques in detail, the book also provides a real implementation of these methods and techniques using the power of TensorFlow and PyTorch. The book covers some enticing projects that show the power of reinforcement learning, and not to mention that everything is concise, up-to-date, and visually explained. After finishing this book, the reader will have a thorough, intuitive understanding of modern reinforcement learning and its applications, which will tremendously aid them in delving into the interesting field of reinforcement learning. WHAT YOU WILL LEARN ? Familiarize yourself with the fundamentals of Reinforcement Learning and Deep Reinforcement Learning. ? Make use of Python and Gym framework to model an external environment. ? Apply classical Q-learning, Monte Carlo, Policy Gradient, and Thompson sampling techniques. ? Explore TensorFlow and PyTorch to practice the fundamentals of deep reinforcement learning. ? Design a smart agent for a particular problem using a specific technique. WHO THIS BOOK IS FOR This book is for machine learning engineers, deep learning fanatics, AI software developers, data scientists, and other data professionals eager to learn and apply Reinforcement Learning to ongoing projects. No specialized knowledge of machine learning is necessary; however, proficiency in Python is desired. AUTHOR BIO Ivan Gridin is a researcher, author, developer, and artificial intelligence expert who has worked on distributive high-load systems and implemented different machine learning approaches in practice. One of the primary areas of his research is the design and development of predictive time series models. Ivan has fundamental math skills in random process theory, time series analysis, machine learning, reinforcement learning, neural architecture search, and optimization. He has published books on genetic algorithms and time series forecasting. He is a loving husband and father and collector of old math books.
Leggi di più Leggi di meno

Dettagli

2022
Testo in en
Tutti i dispositivi (eccetto Kindle) Scopri di più
Reflowable
9789355512055
Chiudi
Aggiunto

L'articolo è stato aggiunto al carrello

Compatibilità

Formato:

Gli eBook venduti da IBS.it sono in formato ePub e possono essere protetti da Adobe DRM. In caso di download di un file protetto da DRM si otterrà un file in formato .acs, (Adobe Content Server Message), che dovrà essere aperto tramite Adobe Digital Editions e autorizzato tramite un account Adobe, prima di poter essere letto su pc o trasferito su dispositivi compatibili.

Compatibilità:

Gli eBook venduti da IBS.it possono essere letti utilizzando uno qualsiasi dei seguenti dispositivi: PC, eReader, Smartphone, Tablet o con una app Kobo iOS o Android.

Cloud:

Gli eBook venduti da IBS.it sono sincronizzati automaticamente su tutti i client di lettura Kobo successivamente all’acquisto. Grazie al Cloud Kobo i progressi di lettura, le note, le evidenziazioni vengono salvati e sincronizzati automaticamente su tutti i dispositivi e le APP di lettura Kobo utilizzati per la lettura.

Clicca qui per sapere come scaricare gli ebook utilizzando un pc con sistema operativo Windows

Chiudi

Aggiungi l'articolo in

Chiudi
Aggiunto

L’articolo è stato aggiunto alla lista dei desideri

Chiudi

Crea nuova lista

Chiudi

Chiudi

Siamo spiacenti si è verificato un errore imprevisto, la preghiamo di riprovare.

Chiudi

Verrai avvisato via email sulle novità di Nome Autore