Deep Reinforcement Learning Hands-On: Apply modern RL...

Main
Computers - Computer Science
Deep Reinforcement Learning Hands-On:...

Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Maxim Lapan

5.0 / 0

Bu kitabı ne kadar beğendiniz?

İndirilen dosyanın kalitesi nedir?

Kalitesini değerlendirmek için kitabı indirin

İndirilen dosyaların kalitesi nedir?

Kategoriler:

Computers - Computer Science

Yıl:

2018

Baskı:

1

Yayımcı:

Packt Publishing

Dil:

english

ISBN 10:

1788834240

ISBN 13:

9781788834247

Seriler:

Expert Insight

Dosya:

EPUB, 12.69 MB

IPFS:

,

english, 2018

İndir (epub, 12.69 MB)

'e dönüştürme devam ediyor

dosyasına dönüştürme başarısız oldu

İlginizi çekebilir

全国政协文史资料委员会 — 文史资料存稿选编(15) 军事机构(上)

Pratap Dangeti — Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

Michael Nielsen — Neural Networks and Deep Learning

Jason Brownlee — Long Short-Term Memory Networks With Python: Develop Sequence Prediction Models With Deep Learning

Laura Graesser; Wah Loon Keng — Foundations of Deep Reinforcement Learning: Theory and Practice in Python

Christopher M. Bishop — Neural Networks for Pattern Recognition

Sutton, Richard S., Barto, Andrew G. — Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)

H. M. Schwartz — Multi-Agent Machine Learning: A Reinforcement Approach

Masashi Sugiyama — Statistical Reinforcement Learning: Modern Machine Learning Approaches

Taweh Beysolow — Applied Reinforcement Learning with Python. With OpenAI Gym, Tensorflow and Keras

J. Patterson, A. Gibson — Deep Learning: A Practitioner's Approach

Aurelien Geron — Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande — Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More

Katy Warr — Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery

Ian Pointer — Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications

Jeremy Howard, Sylvain Gugger — Deep Learning for Coders With Fastai and Pytorch: AI Applications Without a PhD

N D Lewis — Deep Time Series Forecasting with Python: An Intuitive Introduction to Deep Learning for Applied Time Series Modeling

Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin — Learning From Data: A short course

Tommaso Teofili — Deep Learning for Search

Alexander Zai, Brandon Brown — Deep Reinforcement Learning in Action

Miguel Morales — Grokking Deep Reinforcement Learning

Jakub Langr, Vladimir Bok — GANs in Action

Farrukh Akhtar — Practical Reinforcement Learning

Antonio Gulli, Sujit Pal — Deep Learning with Keras: Implementing Deep Learning Models and Neural Networks with the power of Python

Farrukh Akhtar — Practical Reinforcement Learning: Develop self-evolving, intelligent agents with OpenAI Gym, Python and Java

Krishna Bhavsar, Pratap Dangeti — Natural Language Processing with Python Cookbook

Rajalingappaa Shanmugamani — Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras

Manpreet Singh Ghotra, Rajdeep Dua — Neural Network Programming with TensorFlow

Kuntal Ganguly — Learning generative adversarial networks : next-generation deep learning simplified

Vishnu Subramanian — Deep Learning with PyTorch

Lapan, Maxim — Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

M. Arif Wani, Farooq Ahmad Bhat, Saduf Afzal, Asif Iqbal Khan — Advances in Deep Learning

Phil Winder Ph. D. — Reinforcement Learning

Phil Winder — Reinforcement Learning

Nimish Sanghi — Deep reinforcement learning with Python : with Pytorch, Tensorflow and OpenAI Gym

Lapan, Maxim — Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Ivan Gridin — Practical Deep Reinforcement Learning with Python

Nimish Sanghi — Deep Reinforcement Learning with Python: RLHF for Chatbots and Large Language Models, 2nd Edition

Nimish Sanghi — Deep Reinforcement Learning with Python: RLHF for Chatbots and Large Language Models

Lapan, Maxim — Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Phil Winder Ph. D. [D., Phil Winder Ph.] — Reinforcement Learning

Anahtar ifadeler

İlgili Koleksiyonlar