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Applied Reinforcement Learning with Python
Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions. What You'll Learn Implement reinforcement learning with Python Work with AI frameworks such as OpenAI Gym, Tensorflow, and KerasDeploy and train reinforcement learning–based solutions via cloud resourcesApply practical applications of reinforcement learning Who This Book Is For Data scientists, machine learning engineers and software engineers familiar with machine learning and deep learning concepts.
E-Business and Telecommunications
This book constitutes the refereed proceedings of the 10th International Joint Conference on E-Business and Telecommunications, ICETE 2013, held in Reykjavik, Iceland, in July 2013. ICETE is a joint international conference integrating four major areas of knowledge that are divided into six corresponding conferences: International Conference on Data Communication Networking, DCNET; International Conference on E-Business, ICE-B; International Conference on Optical Communication Systems, OPTICS; International Conference on Security and Cryptography, SECRYPT; International Conference on Wireless Information Systems, WINSYS; and International Conference on Signal Processing and Multimedia, SIGMAP. The 24 full papers presented were carefully reviewed and selected from 341 submissions. The papers cover the following key areas of e-business and telecommunications: data communication networking, e-business, optical communication systems, security and cryptography, signal processing and multimedia applications, wireless information networks and systems.
Parallel Problem Solving from Nature - PPSN IX
This book constitutes the refereed proceedings of the 9th International Conference on Parallel Problem Solving from Nature, PPSN 2006. The book presents 106 revised full papers covering a wide range of topics, from evolutionary computation to swarm intelligence and bio-inspired computing to real-world applications. These are organized in topical sections on theory, new algorithms, applications, multi-objective optimization, evolutionary learning, as well as representations, operators, and empirical evaluation.
2020 IEEE Asia Pacific Conference on Computer Science and Data Engineering (CSDE)
The conference title is belonging 100 in the area of IEEE Computer Society This event would be a wonderful gathering between IEEE members in the area of South Pacific, Australia and the rest of the world to share the latest development in the area of computer science and data engineering The conference will be a forum for participants to discuss state of the art innovations in technologies which have been made available by the researchers & IT professionals and will feature plenary and panel sessions as well as technical paper presentations and poster sessions Workshop by international experts on ICT and Data Engineering applications will also be available The conference theme for 2020 is Visualise the Future through Data CSDE 2020 is also aimed to promote discussion about the pedagogical potential of new sustainable technologies for the developing countries
Reinforcement Learning with TensorFlow
Leverage the power of the Reinforcement Learning techniques to develop self-learning systems using Tensorflow Key Features Learn reinforcement learning concepts and their implementation using TensorFlow Discover different problem-solving methods for Reinforcement Learning Apply reinforcement learning for autonomous driving cars, robobrokers, and more Book Description Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacent...
Deep Reinforcement Learning
Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research ...
Hands-On Intelligent Agents with OpenAI Gym
Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulator Key Features Explore the OpenAI Gym toolkit and interface to use over 700 learning tasks Implement agents to solve simple to complex AI problems Study learning environments and discover how to create your own Book Description Many real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an...
Computer Vision – ECCV 2018
The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
