Biznes IT
Alvaro Fuentes
Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages.The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model.Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming.
Anubhav Singh, Sayak Paul
When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you'll cover the latest tools and technological practices that are being used to implement deep learning in web development using Python.Starting with the fundamentals of machine learning, you'll focus on DL and the basics of neural networks, including common variants such as convolutional neural networks (CNNs). You'll learn how to integrate them into websites with the frontends of different standard web tech stacks. The book then helps you gain practical experience of developing a deep learning-enabled web app using Python libraries such as Django and Flask by creating RESTful APIs for custom models. Later, you'll explore how to set up a cloud environment for deep learning-based web deployments on Google Cloud and Amazon Web Services (AWS). Next, you'll learn how to use Microsoft's intelligent Emotion API, which can detect a person's emotions through a picture of their face. You'll also get to grips with deploying real-world websites, in addition to learning how to secure websites using reCAPTCHA and Cloudflare. Finally, you'll use NLP to integrate a voice UX through Dialogflow on your web pages.By the end of this book, you'll have learned how to deploy intelligent web apps and websites with the help of effective tools and practices.
Hands-On Q-Learning with Python. Practical Q-learning with OpenAI Gym, Keras, and TensorFlow
Nazia Habib
Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers.This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you become familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into model-free Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in scientific research. Toward the end, you’ll gain insight into what’s in store for reinforcement learning.By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow.
Rounak Banik
Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible..In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains.
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.
Hurtownie danych. Od przetwarzania analitycznego do raportowania
Adam Pelikant
Spec od hurtowni danych? Zawsze będzie pilnie potrzebny! Jak stworzyć strukturę hurtowni danych i dokonać ich integracji? Jak przeprowadzić analizę danych z wykorzystaniem rozszerzenia MDX SQL? Do czego potrzebne jest raportowanie? Idea hurtowni danych ściśle wiąże się z ich kolosalnymi ilościami, gromadzonymi podczas tysięcy różnych sytuacji — przy dowolnej transakcji, w urzędzie, na lotnisku, w internecie… Nawet nasze połączenia telefoniczne są przechowywane przez operatora. Te wszystkie dane trzeba gdzieś pomieścić, sensownie posegregować i zapewnić sobie możliwość sięgnięcia do wybranego ich zakresu bez długotrwałych poszukiwań. Taką możliwość dają właśnie hurtownie danych — przemyślane, bardzo pojemne bazy, oferujące zarówno integrację wprowadzanych danych, jak i znakomite mechanizmy ich przeszukiwania. Jeśli chcesz poszerzyć swoją wiedzę na temat tworzenia i przeglądania zawartości hurtowni danych, trafiłeś pod właściwy adres! Książka "Hurtownie danych. Od przetwarzania analitycznego do raportowania" zawiera materiał przeznaczony nie tylko dla studentów wydziałów informatycznych, ale także dla pasjonatów tej tematyki oraz specjalistów zainteresowanych poszerzeniem wiedzy. W możliwie najprostszy, praktyczny sposób opisano w niej składnię i postać zapytań analitycznych, strukturę hurtowni danych oraz kwestię ich integracji i wizualnego tworzenia elementów hurtowni. Znajdziesz tu także omówienie analizy danych z wykorzystaniem rozszerzenia MDX SQL oraz zastosowań raportowania. Zapoznanie się z tymi informacjami oraz prześledzenie zgromadzonych tu przykładów pozwoli Ci zrozumieć problemy powstające przy budowie hurtowni danych i wykorzystać tę wiedzę we własnych projektach. Zapytania analityczne Struktura hurtowni danych Integracja danych Wizualne tworzenie elementów hurtowni danych Analiza danych z wykorzystaniem rozszerzenia MDX SQL Raportowanie Od bazy do hurtowni danych… Skocz na głęboką wodę!
IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI
Hemanth Manda, Sriram Srinivasan, Deepak Rangarao
Cloud Pak for Data is IBM's modern data and AI platform that includes strategic offerings from its data and AI portfolio delivered in a cloud-native fashion with the flexibility of deployment on any cloud. The platform offers a unique approach to addressing modern challenges with an integrated mix of proprietary, open-source, and third-party services.You'll begin by getting to grips with key concepts in modern data management and artificial intelligence (AI), reviewing real-life use cases, and developing an appreciation of the AI Ladder principle. Once you've gotten to grips with the basics, you will explore how Cloud Pak for Data helps in the elegant implementation of the AI Ladder practice to collect, organize, analyze, and infuse data and trustworthy AI across your business. As you advance, you'll discover the capabilities of the platform and extension services, including how they are packaged and priced. With the help of examples present throughout the book, you will gain a deep understanding of the platform, from its rich capabilities and technical architecture to its ecosystem and key go-to-market aspects.By the end of this IBM book, you'll be able to apply IBM Cloud Pak for Data's prescriptive practices and leverage its capabilities to build a trusted data foundation and accelerate AI adoption in your enterprise.
Keith McCormick, Jesus Salcedo
IBM SPSS Modeler allows users to quickly and efficiently use predictive analytics and gain insights from your data. With almost 25 years of history, Modeler is the most established and comprehensive Data Mining workbench available. Since it is popular in corporate settings, widely available in university settings, and highly compatible with all the latest technologies, it is the perfect way to start your Data Science and Machine Learning journey. This book takes a detailed, step-by-step approach to introducing data mining using the de facto standard process, CRISP-DM, and Modeler’s easy to learn “visual programming” style. You will learn how to read data into Modeler, assess data quality, prepare your data for modeling, find interesting patterns and relationships within your data, and export your predictions. Using a single case study throughout, this intentionally short and focused book sticks to the essentials. The authors have drawn upon their decades of teaching thousands of new users, to choose those aspects of Modeler that you should learn first, so that you get off to a good start using proven best practices. This book provides an overview of various popular data modeling techniques and presents a detailed case study of how to use CHAID, a decision tree model. Assessing a model’s performance is as important as building it; this book will also show you how to do that. Finally, you will see how you can score new data and export your predictions. By the end of this book, you will have a firm understanding of the basics of data mining and how to effectively use Modeler to build predictive models.
James D. Miller
IBM Watson provides fast, intelligent insight in ways that the human brain simply can't match. Through eight varied projects, this book will help you explore the computing and analytical capabilities of IBM Watson.The book begins by refreshing your knowledge of IBM Watson's basic data preparation capabilities, such as adding and exploring data to prepare it for being applied to models. The projects covered in this book can be developed for different industries, including banking, healthcare, media, and security. These projects will enable you to develop an AI mindset and guide you in developing smart data-driven projects, including automating supply chains, analyzing sentiment in social media datasets, and developing personalized recommendations.By the end of this book, you'll have learned how to develop solutions for process automation, and you'll be able to make better data-driven decisions to deliver an excellent customer experience.
James D. Miller
IBM Watson provides fast, intelligent insight in ways that the human brain simply can't match. Through eight varied projects, this book will help you explore the computing and analytical capabilities of IBM Watson.The book begins by refreshing your knowledge of IBM Watson's basic data preparation capabilities, such as adding and exploring data to prepare it for being applied to models. The projects covered in this book can be developed for different industries, including banking, healthcare, media, and security. These projects will enable you to develop an AI mindset and guide you in developing smart data-driven projects, including automating supply chains, analyzing sentiment in social media datasets, and developing personalized recommendations.By the end of this book, you'll have learned how to develop solutions for process automation, and you'll be able to make better data-driven decisions to deliver an excellent customer experience.