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Essential Emergency Trauma
Essential Emergency Trauma is a concise, reader-friendly, and portable reference on the care of trauma patients in the emergency department. Geared to practicing emergency physicians, residents, and medical students. Major sections cover trauma of each anatomic region. Each section opens with a chapter "The First 15 Minutes, Algorithm, and Decision Making." Subsequent chapters focus on specific injury patterns, emphasizing pathophysiology, diagnosis, evaluation, and management. The information is presented in bullet points with numerous tables and images. Each chapter ends with an up-to-date review of the "Best Evidence."
MEDINFO 2019: Health and Wellbeing e-Networks for All
Combining and integrating cross-institutional data remains a challenge for both researchers and those involved in patient care. Patient-generated data can contribute precious information to healthcare professionals by enabling monitoring under normal life conditions and also helping patients play a more active role in their own care. This book presents the proceedings of MEDINFO 2019, the 17th World Congress on Medical and Health Informatics, held in Lyon, France, from 25 to 30 August 2019. The theme of this year’s conference was ‘Health and Wellbeing: E-Networks for All’, stressing the increasing importance of networks in healthcare on the one hand, and the patient-centered perspectiv...
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...
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...
Python Reinforcement Learning Projects
Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries Key FeaturesImplement Q-learning and Markov models with Python and OpenAIExplore the power of TensorFlow to build self-learning modelsEight AI projects to gain confidence in building self-trained applicationsBook Description Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years. In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorit...
Computer Security – ESORICS 2022
The three volume set LNCS 13554, 13555, 13556 constitutes the proceedings of the 27th European Symposium on Research in Computer Security, ESORICS 2022, which took place in September 2022. The conference took place in Copenhagen, Denmark, in a hybrid mode. The 104 full papers and 6 poster papers presented in these proceedings were carefully reviewed and selected from 562 submissions. They were organized in topical sections as follows: Part I: Blockchain security; privacy; crypto; attacks; sidechannels; Part II: Anonymity; cloud security; access control; authentication; digital signatures; IoT security; applications; Part III: Formal analysis; Web security; hardware security; multiparty computation; ML techniques; cyber-physical systems security; network and software security; posters.
It's Festival Time!
The book introduces the reader to the major festivals celebrated around the world, especially in India. It also gives a brief write-up on how winter festivals were celebrated before and after the advent of Christianity. It mentions well known legends related to festivals and concludes with a few contemporary stories, each with a festival as its central theme. What do festivals, in general, symbolize? Which festivals are specially meant for brothers and sisters? How were winter festivals celebrated around the world before the advent of Christianity? How did the tradition of the Christmas Tree come about? Why is Ramewaram known as the Agni-teertham? What is special about the popular Christmas Carroll, “Silent Night”? What gives Sudeep a new meaning to Holi? How does Sayan discover the true message of Dussera? How does the “Face Book” enable Neeti to find the best Eid gift for Zubie Khala?
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.
