Mastering Reinforcement Learning with Python : Build next-generation, self-learning models using reinforcement learning techniques and best practices

Mastering Reinforcement Learning with Python : Build next-generation, self-learning models using reinforcement learning techniques and best practices

Enregistré dans:

Détails bibliographiques
Auteur principal:	Bilgin, Enes. (Auteur)
Support:	E-Book
Langue:	Anglais
Publié:	Birmingham : Packt Publishing, 2020.
Autres localisations:	Voir dans le Sudoc
Accès en ligne:	Accès à l'E-Book
Lien:	Autre support: Mastering Reinforcement Learning with Python : Build next-generation, self-learning models using reinforcement learning techniques and best practices / Enes Bilgin

Documents similaires

Hands-on reinforcement learning with Python : master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow
par: Ravichandiran, Sudharsan.
Publié: (2018)

Python reinforcement learning : solve complex real-world problems by mastering reinforcement learning algorithms using OpenAI Gym and TensorFlow
Publié: (2019)

Python reinforcement learning projects : eight hands-on projects exploring reinforcement learning algorithms using TensorFlow
par: Saito, Sean.
Publié: (2018)

Keras reinforcement learning projects : 9 projects exploring popular reinforcement learning techniques to build self-learning agents
par: Ciaburro, Giuseppe (19..-....).
Publié: (2018)

Reinforcement Learning Algorithms with Python : Learn, understand, and develop smart algorithms for addressing AI challenges
par: Lonza, Andrea.
Publié: (2019)

Hands-On Reinforcement Learning for Games : Implementing self-learning agents in games using artiﬁcial intelligence techniques
par: Lanham, Micheal.
Publié: (2020)

Practical Deep Reinforcement Learning with Python : Concise Implementation of Algorithms, Simplified Maths, and Effective Use of TensorFlow and PyTorch
par: Gridin, Ivan.

The Reinforcement Learning Workshop : Learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems
par: Palmas, Alessandro.
Publié: (2020)

Deep Reinforcement Learning with Python : Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow
par: Ravichandiran, Sudharsan.
Publié: (2020)

PyTorch 1.x reinforcement learning cookbook : over 60 recipes to design, develop, and deploy self-learning AI models using Python
par: Liu, Yuxi (Hayden).
Publié: (2019)

Reinforcement learning with TensorFlow : a beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym
par: Dutta, Sayon.
Publié: (2018)

Deep Reinforcement Learning Hands-On : Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more
par: Lapan, Maxim.
Publié: (2020)

TensorFlow 2 Reinforcement Learning Cookbook : Over 50 recipes to help you build, train, and deploy learning agents for real-world applications
par: Palanisamy, Praveen.
Publié: (2021)

TensorFlow Reinforcement Learning Quick Start Guide : Build and manage any business on top of the Google Cloud infrastructure
par: Balakrishnan, Kaushik.
Publié: (2019)

Reinforced Concrete Design
par: Aghayere, Abi.

Deep Learning with TensorFlow and Keras : Build and deploy supervised, unsupervised, deep, and reinforcement learning models
par: Kapoor, Amita.
Publié: (2022)

Biofiber reinforcement in composite materials
Publié: (2014)

Hands-On Ensemble Learning with Python : Build highly optimized ensemble machine learning models using scikit-learn and Keras
par: Kyriakides, George.
Publié: (2019)

R machine learning projects : implement supervised, unsupervised, and reinforcement learning techniques using R 3.5
par: Chinnamgari, Sunil Kumar.
Publié: (2019)

Machine Learning Engineering with Python : Manage the lifecycle of machine learning models using MLOps with practical examples
par: Mcmahon, Andrew P..

Learn AI with Python : Explore Machine Learning and Deep Learning techniques for Building Smart AI Systems Using Scikit-Learn, NLTK, NeuroLab, and Keras
par: Leekha, Gaurav.

Mastering predictive analytics with scikit-learn and TensorFlow : implement machine learning techniques to build advanced predictive models using Python
par: Fontaine, Alan.
Publié: (2018)

Building Machine Learning Systems with Python : Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow
par: Coelho, Luis Pedro.
Publié: (2018)

Building Machine Learning Systems Using Python : Practice to Train Predictive Models and Analyze Machine Learning Results with Real Use-Cases
par: Chopra, Deepti (19..-....).

Glassfibre reinforced concrete : principles, production, properties and applications
par: Bartos, Peter.
Publié: (2017)

Hands-on deep learning for games : leverage the power of neural networks and reinforcement learning to build intelligent games
par: Lanham, Micheal.
Publié: (2019)

Machine Learning Engineering with Python : Manage the production life cycle of machine learning models using MLOps with practical examples
par: Mcmahon, Andrew P.
Publié: (2021)

Machine Learning with Python : Design and Develop Machine Learning and Deep Learning Technique using real world code examples
par: Vijayvargia, Abhishek.

Hands-on unsupervised learning with Python : implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more
par: Bonaccorso, Giuseppe.
Publié: (2019)

Building Machine Learning Systems with Python : master the art of machine learning with Python and build effective machine learning systems with this intensive hands-on guide
par: Richert, Willi.
Publié: (2013)

Applied Supervised Learning with Python : Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning
par: Johnston, Benjamin.
Publié: (2019)

Learning Python
par: Romano, Fabrizio, 1975-
Publié: (2015)

Learning Geospatial Analysis with Python : Unleash the power of Python 3 with practical techniques for learning GIS and remote sensing
par: Lawhead, Joel.

Hands-on Supervised Learning with Python : Learn How to Solve Machine Learning Problems with Supervised Learning Algorithms Using Python
par: Shang, Madeleine.

Carbon nanotube-reinforced polymers : from nanoscale to macroscale
Publié: (2017)

Pragmatic Machine Learning with Python : Learn How to Deploy Machine Learning Models in Production
par: Nag, Avishek.

Advanced Deep Learning with Python : Design and implement advanced next-generation AI solutionsusing TensorFlow and PyTorch
par: Vasilev, Ivan.
Publié: (2019)

Python Machine Learning : Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2
par: Raschka, Sebastian.
Publié: (2019)

Mastering machine learning for penetration testing : develop an extensive skill set to break self-learning systems using Pytho
par: Chebbi, Chiheb.
Publié: (2018)

Promoting democracy, reinforcing authoritarianism : US and European policy in Jordan
par: Schuetze, Benjamin (1986-....).

Université catholique de l'Ouest - Bibliothèques - 3 place André Leroy - BP10808 - 49008 ANGERS Cedex 1