Biznes IT
Sudharsan Ravichandiran
Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning.By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
Sudharsan Ravichandiran
Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning.By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
Brian Amos
A real-time operating system (RTOS) is used to develop systems that respond to events within strict timelines. Real-time embedded systems have applications in various industries, from automotive and aerospace through to laboratory test equipment and consumer electronics. These systems provide consistent and reliable timing and are designed to run without intervention for years.This microcontrollers book starts by introducing you to the concept of RTOS and compares some other alternative methods for achieving real-time performance. Once you've understood the fundamentals, such as tasks, queues, mutexes, and semaphores, you'll learn what to look for when selecting a microcontroller and development environment. By working through examples that use an STM32F7 Nucleo board, the STM32CubeIDE, and SEGGER debug tools, including SEGGER J-Link, Ozone, and SystemView, you'll gain an understanding of preemptive scheduling policies and task communication. The book will then help you develop highly efficient low-level drivers and analyze their real-time performance and CPU utilization. Finally, you'll cover tips for troubleshooting and be able to take your new-found skills to the next level.By the end, you'll have built on your embedded system skills and will be able to create real-time systems using microcontrollers and FreeRTOS.
Giuseppe Ciaburro
Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python.Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks.By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.
Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh
Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.
Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh
Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.
Giuseppe Bonaccorso
Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images.By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.
Giuseppe Bonaccorso
Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images.By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.
Chandra Mohan Dhanasekaran, Manjunath H. Gowda
This HashiCorp Terraform Associate (003) exam guide simplifies technical content relating to the exam and helps you learn using real-world examples. The book is aligned with the latest exam objectives, which enables you to streamline your learning experience instead of referring to multiple sources for preparation. Moreover, the book is designed to serve as a one-stop solution for readers with varied levels of experience in Terraform.You'll learn how to efficiently provision and manage cloud and on-premises infrastructure using Terraform. As you progress, you’ll focus on essential commands, state management techniques, and best practices. Later chapters will show you how to harness the power of Terraform modules for code reusability and scalability. You’ll also gain insights into advanced topics such as debugging, troubleshooting, and leveraging Terraform Cloud and Terraform Enterprise for collaborative infrastructure management. This book provides you with lifetime access to supplementary practice resources such as mock exams, flashcards, and exam tips from experts.By the end of this book, you’ll have the knowledge and skills you need to confidently tackle the Terraform Associate certification exam and excel in your career.
Chandra Mohan Dhanasekaran, Manjunath H. Gowda
This HashiCorp Terraform Associate (003) exam guide simplifies technical content relating to the exam and helps you learn using real-world examples. The book is aligned with the latest exam objectives, which enables you to streamline your learning experience instead of referring to multiple sources for preparation. Moreover, the book is designed to serve as a one-stop solution for readers with varied levels of experience in Terraform.You'll learn how to efficiently provision and manage cloud and on-premises infrastructure using Terraform. As you progress, you’ll focus on essential commands, state management techniques, and best practices. Later chapters will show you how to harness the power of Terraform modules for code reusability and scalability. You’ll also gain insights into advanced topics such as debugging, troubleshooting, and leveraging Terraform Cloud and Terraform Enterprise for collaborative infrastructure management. This book provides you with lifetime access to supplementary practice resources such as mock exams, flashcards, and exam tips from experts.By the end of this book, you’ll have the knowledge and skills you need to confidently tackle the Terraform Associate certification exam and excel in your career.
Michał Wawrzyniak
Hejt do niedawna stanowił wyłącznie czarną stronę sieci. Przyzwyczailiśmy się do niego, a nawet niesłusznie go zaakceptowaliśmy. Efekt? Hejt wyszedł z monitorów na ulice, a my musimy żyć w świecie, w którym każdy może obrobić nam tyłek i zrobić krzywdę — nie tylko słowem, ale i czynem. Hejter rzuca bezpodstawne, agresywne i krzywdzące opinie na prawo i lewo. Kiedyś cierpiały na tym dzieci w gimnazjach, później prezenterzy telewizyjni. Dziś problem ten niebezpiecznie zbliża się do Ciebie. Gwałtownie topnieją szanse na to, abyś nigdy nie poczuł jego niszczycielskiego wpływu. Co gorsza, zrozumiesz wagę problemu dopiero wtedy, gdy dotknie Cię osobiście. Ten czas nadejdzie, prędzej czy później. Wszyscy doskonale wiemy, że wokół nas żyją ludzie głupi, bezduszni i agresywni. Dlaczego pokornie zgadzamy się na ich obecność w swoim otoczeniu? Nie możemy ich wyeliminować, ale czy naprawdę mamy pozwolić, by zdominowali nas na amen? Może warto otworzyć oczy i coś z tym zrobić? Autor tej książki otwiera ludziom oczy od wielu lat. Co więcej, zaplanował i poprowadził największą w Polsce kampanię crowdfundingową. Ustanowił nowy rekord, który zwrócił na problem hejtu uwagę mediów i otworzył drzwi wielu innym, ważnym społecznie projektom. Pokazał, jak działa jego technika: z minusa na plus. Już wkrótce sam się jej nauczysz. W książce znajdziesz zresztą dużo więcej przydatnych technik, a każda z nich pomoże Ci nie tylko w walce z hejtem, lecz także w osobistym rozwoju.
Michał Wawrzyniak
Hejt do niedawna stanowił wyłącznie czarną stronę sieci. Przyzwyczailiśmy się do niego, a nawet niesłusznie go zaakceptowaliśmy. Efekt? Hejt wyszedł z monitorów na ulice, a my musimy żyć w świecie, w którym każdy może obrobić nam tyłek i zrobić krzywdę — nie tylko słowem, ale i czynem. Hejter rzuca bezpodstawne, agresywne i krzywdzące opinie na prawo i lewo. Kiedyś cierpiały na tym dzieci w gimnazjach, później prezenterzy telewizyjni. Dziś problem ten niebezpiecznie zbliża się do Ciebie. Gwałtownie topnieją szanse na to, abyś nigdy nie poczuł jego niszczycielskiego wpływu. Co gorsza, zrozumiesz wagę problemu dopiero wtedy, gdy dotknie Cię osobiście. Ten czas nadejdzie, prędzej czy później. Wszyscy doskonale wiemy, że wokół nas żyją ludzie głupi, bezduszni i agresywni. Dlaczego pokornie zgadzamy się na ich obecność w swoim otoczeniu? Nie możemy ich wyeliminować, ale czy naprawdę mamy pozwolić, by zdominowali nas na amen? Może warto otworzyć oczy i coś z tym zrobić? Autor tej książki otwiera ludziom oczy od wielu lat. Co więcej, zaplanował i poprowadził największą w Polsce kampanię crowdfundingową. Ustanowił nowy rekord, który zwrócił na problem hejtu uwagę mediów i otworzył drzwi wielu innym, ważnym społecznie projektom. Pokazał, jak działa jego technika: z minusa na plus. Już wkrótce sam się jej nauczysz. W książce znajdziesz zresztą dużo więcej przydatnych technik, a każda z nich pomoże Ci nie tylko w walce z hejtem, lecz także w osobistym rozwoju.
Human versus Machine: Accounting, Auditing and Education in the Era of Artificial Intelligence
Joanna Dyczkowska red.
The present book provides a critical reflection on the state and perspectives of the accountancy and auditors profession, addresses the AI technology's supportive role in accounting and auditing tasks, reporting processes and reports' attestation, as well as stresses the necessity to modify educational curriculums in business accounting in response to changing circumstances, and set the regulatory frameworks regarding the use of generative AI by academics and students. The considerations covered in the book are timely and can be helpful for business practitioners, academics and students. Moreover, the international context of the publication, reference to the latest literature on AI applications in business accounting, and presented research study can make this work valuable for all those interested in the opportunities and threats that AI may bring into the professional lives of many.