Big data
Pablo Sáenz de Tejada, Daria Kirilenko
In today’s data-driven business world, advanced analytics set organizations apart. Basic visualizations no longer suffice for Tableau developers tackling complex data challenges. Written by Tableau experts who've trained Fortune 500 companies and led global analytics initiatives, this cookbook delivers battle-tested techniques with the perfect blend of technical depth and practical application.You’ll master advanced techniques such as geospatial analysis, data modeling for optimized workflows, and enterprise-scale content management. This book shows you how to leverage Tableau cloud’s Data Management capabilities to centralize data sources and ensure data quality for consistent analytics. You’ll also explore advanced management features such as the Content Migration Tool in Tableau 2025.1 and beyond.Bridging the gap between fundamentals and cutting-edge practices, this book extends Tableau’s capabilities with APIs, custom LOD expressions, virtual connections, data apps, and TabPy. You’ll gain the skills to solve complex business problems, create high-impact dashboards, and seamlessly integrate Tableau into your data strategy, all while adhering to security and governance best practices.*Email sign-up and proof of purchase required
Tableau: Creating Interactive Data Visualizations. Creating Interactive Data Visualizations
Matt Floyd, Jen Stirrup, Ashley Ohmann, Ashutosh...
With increasing interest for data visualization in the media, businesses are looking to create effective dashboards that engage as well as communicate the truth of data. Tableau makes data accessible to everyone, and is a great way of sharing enterprise dashboards across the business. Tableau is a revolutionary toolkit that lets you simply and effectively create high-quality data visualizations.This course starts with making you familiar with its features and enable you to develop and enhance your dashboard skills, starting with an overview of what dashboard is, followed by how you can collect data using various mathematical formulas. Next, you'll learn to filter and group data, as well as how to use various functions to present the data in an appealing and accurate way. In the first module, you will learn how to use the key advanced string functions to play with data and images. You will be walked through the various features of Tableau including dual axes, scatterplot matrices, heat maps, and sizing.In the second module, you’ll start with getting your data into Tableau, move onto generating progressively complex graphics, and end with the finishing touches and packaging your work for distribution. This module is filled with practical examples to help you create filled maps, use custom markers, add slider selectors, and create dashboards. You will learn how to manipulate data in various ways by applying various filters, logic, and calculating various aggregate measures. Finally, in the third module, you learn about Tableau Public using which allows readers to explore data associations in multiple-sourced public data, and uses state-of-the-art dashboard and chart graphics to immerse the users in an interactive experience. In this module, the readers can quickly gain confidence in understanding and expanding their visualization, creation knowledge, and quickly create interesting, interactive data visualizations to bring a richness and vibrancy to complex articles.The course provides a great overview for beginner to intermediate Tableau users, and covers the creation of data visualizations of varying complexities.
Dmitry Anoshin, JC Gillet, Fabian Peri, Radhika...
The Tableau Desktop Certified Associate exam measures your knowledge of Tableau Desktop and your ability to work with data and data visualization techniques. This book will help you to become well-versed in Tableau software and use its business intelligence (BI) features to solve BI and analytics challenges.With the help of this book, you'll explore the authors' success stories and their experience with Tableau. You'll start by understanding the importance of Tableau certification and the different certification exams, along with covering the exam format, Tableau basics, and best practices for preparing data for analysis and visualization. The book builds on your knowledge of advanced Tableau topics such as table calculations for solving problems. You'll learn to effectively visualize geographic data using vector maps. Later, you'll discover the analytics capabilities of Tableau by learning how to use features such as forecasting. Finally, you'll understand how to build and customize dashboards, while ensuring they convey information effectively. Every chapter has examples and tests to reinforce your learning, along with mock tests in the last section.By the end of this book, you'll be able to efficiently prepare for the certification exam with the help of mock tests, detailed explanations, and expert advice from the authors.
Te małe seksowne cyferki. Jak rozwinąć biznes przy użyciu danych, które już mamy
Paul Brown, Dimitri Maex
Czy możesz sobie wyobrazić, że jesteś w stanie zidentyfikować klientów przynoszących największe zyski, wypracować lepszą strategię komunikowania się z nimi i zainspirować ich, aby kupowali więcej? A więc, naprawdę możesz to zrobić. A najlepsze jest to, że możesz to osiągnąć używając danych, które już masz. Wszystko, co robimy, tworzy dane. Za każdym razem, gdy ktoś ogląda coś w sieci, szuka w Google lub nawet przegląda sieć w swoim telefonie, tworzona jest kolejna cząstka danych, które mogą pomóc nam zrozumieć i przewidzieć zachowanie konsumentów. Rewolucja w analizowaniu danych właśnie się toczy i metody oraz narzędzia radzące sobie z tym „potopem danych” stają się coraz prostsze i mniej kosztowne, a jednocześnie bardziej precyzyjne, niż kiedykolwiek wcześniej. Dimitri Maex, Managing Director globalnej agencji reklamowej OgilvyOne New York i mózg praktyki analitycznej stosowanej w agencji pokazuje, jak możemy przekształcić swoje dane – te małe seksowne cyferki, które mogą zapewnić więcej zysków naszemu biznesowi – w skuteczne strategie prawdziwego wzrostu. W jasnym, przejrzystym stylu wyjaśnia, jak: • Ustalić, którzy klienci mają największy potencjał zwiększenia wartości, po których można spodziewać się większych zakupów, a którzy nie są warci targetowania. • Alokować zasoby marketingowe w najlepszy możliwy sposób. • Przewidzieć, jakich produktów lub usług klienci będą potrzebować w przyszłości. • Zoptymalizować swoją obecność w sieci, aby uzyskać największy zwrot z wyszukiwania. Lektura obowiązkowa dla marketerów starających się uzyskać najwyższy zwrot z inwestycji, właścicieli małych firm pragnących rosnąć szybciej lub kreatywnych twórców pragnących poznać reakcję na swoje działania – i nie tylko.
Tech Trends of the 4th Industrial Revolution. Navigating the Future of Technology in Business
Mercury Learning and Information, D. Pyo, J....
The term 4th Industrial Revolution is often mentioned in the media, but public understanding of its technologies lags behind their rapid development. This book bridges the gap, explaining essential technologies like IoT, blockchain, AI, cloud computing, and big data. It aims to enhance comprehension by minimizing technical content.The book introduces key technologies and their applications, emphasizing their importance in contemporary business models. Readers will find accessible descriptions and practical examples to aid understanding. Covering topics and trends vital for modern business, this book ensures readers grasp the technological landscape shaping the future.Throughout the book, you'll explore how these technologies are revolutionizing industries and their integration into business strategies. This journey provides a comprehensive understanding of the 4th Industrial Revolution, equipping readers with the knowledge needed to navigate and leverage these advancements effectively.
TensorFlow. 13 praktycznych projektów wykorzystujących uczenie maszynowe
Ankit Jain, Armando Fandango, Amita Kapoor
TensorFlow służy do projektowania i wdrażania zaawansowanych architektur głębokiego uczenia. Jego zaletami są prostota, wydajność i elastyczność. Umożliwia budowanie złożonych rozwiązań na bazie różnorodnych zbiorów danych. Co więcej, pozwala na stosowanie różnych technik uczenia nadzorowanego, nienadzorowanego oraz uczenia przez wzmacnianie. TensorFlow zmienił sposób postrzegania uczenia maszynowego. Dzięki temu środowisku każdy, kto chce uczynić z dużych zbiorów danych wiarygodne źródło wiedzy, może ten cel osiągnąć - niezależnie od tego, czy jest analitykiem danych, naukowcem, projektantem, czy pasjonatem metod sztucznej inteligencji. To książka przeznaczona dla osób, które chcą nauczyć się tworzyć całościowe rozwiązania z wykorzystaniem uczenia maszynowego. Poszczególne zagadnienia zilustrowano trzynastoma praktycznymi projektami, w których wykorzystano między innymi analizy sentymentów, przetwarzanie języka naturalnego, systemy rekomendacyjne, generatywne sieci kontradyktoryjne czy sieci kapsułowe. Pokazano, w jaki sposób używać TensorFlow z interfejsem APO Spark i wspomagać obliczenia układami GPU. Przedstawiono zastosowanie rozkładu macierzy (SVD++), modeli rankingowych i odmian splotowej sieci neuronowej. Nie zabrakło prezentacji nowych rozwiązań o dużym potencjale, takich jak sieci DiscoGAN. Dołączony do książki kod źródłowy, liczne wskazówki i porady pozwolą na płynne rozpoczęcie pracy z TensorFlow oraz innymi narzędziami do budowy sieci neuronowych. W tej książce między innymi: podstawy pracy z TensorFlow wykorzystanie TensorFlow do wizualizacji sieci neuronowych zastosowanie procesu gaussowskiego do prognozowania cen akcji wykrywanie oszukańczych transakcji za pomocą TensorFlow i Keras implementacja sieci kapsułowych w TensorFlow techniki uczenia przez wzmacnianie TensorFlow: prostota, wydajność i imponujący potencjał!
Antonio Gulli, Amita Kapoor
Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve real-life problems in the artificial intelligence domain.In this book, you will learn how to efficiently use TensorFlow, Google’s open source framework for deep learning. You will implement different deep learning networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs), with easy-to-follow standalone recipes. You will learn how to use TensorFlow with Keras as the backend. You will learn how different DNNs perform onsome popularly used datasets, such as MNIST, CIFAR-10, and Youtube8m. You will not only learn about the different mobile and embedded platforms supported by TensorFlow, but also how to set up cloud platforms for deep learning applications. You will also get a sneak peek at TPU architecture and how it will affect the future of DNNs.By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning,GANs, and autoencoders.
Antonio Gulli, Amita Kapoor
Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve real-life problems in the artificial intelligence domain.In this book, you will learn how to efficiently use TensorFlow, Google’s open source framework for deep learning. You will implement different deep learning networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs), with easy-to-follow standalone recipes. You will learn how to use TensorFlow with Keras as the backend. You will learn how different DNNs perform onsome popularly used datasets, such as MNIST, CIFAR-10, and Youtube8m. You will not only learn about the different mobile and embedded platforms supported by TensorFlow, but also how to set up cloud platforms for deep learning applications. You will also get a sneak peek at TPU architecture and how it will affect the future of DNNs.By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning,GANs, and autoencoders.
TensorFlow 2 Pocket Primer. A Quick Reference Guide for TensorFlow 2 Developers
Mercury Learning and Information, Oswald Campesato
As part of the best-selling *Pocket Primer* series, this book introduces beginners to basic machine learning algorithms using TensorFlow 2. It provides a fast-paced introduction to TensorFlow, covering core features and machine learning basics with Python code samples. An appendix includes Keras-based code samples and explores MLPs, CNNs, RNNs, and LSTMs. The chapters illustrate how to solve various tasks, encouraging further reading to deepen your knowledge.The journey begins with an introduction to TensorFlow 2, followed by essential APIs and datasets. You'll explore linear regression and classifiers, learning to apply TensorFlow to practical problems. The comprehensive appendix covers advanced topics like NLPs and deep learning architectures, enhancing your understanding of machine learning.Understanding these concepts is crucial for modern AI applications. This book transitions readers from basic TensorFlow use to advanced machine learning techniques, blending theory with practical examples. Companion files with source code and figures enhance learning, making this an essential resource for mastering TensorFlow and machine learning.
Tony Holdroyd
TensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks.After giving you an overview of what's new in TensorFlow 2.0 Alpha, the book moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering. You will get familiar with unsupervised learning for autoencoder applications. The book will also show you how to train effective neural networks using straightforward examples in a variety of different domains.By the end of the book, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques.
Tony Holdroyd
TensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks.After giving you an overview of what's new in TensorFlow 2.0 Alpha, the book moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering. You will get familiar with unsupervised learning for autoencoder applications. The book will also show you how to train effective neural networks using straightforward examples in a variety of different domains.By the end of the book, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques.
Abhishek Thakur, Alberto Boschetti, Luca Massaron, Alexey...
TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects.TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. You'll learn how to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing this, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation and use reinforcement learning techniques to play games.By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently.
Abhishek Thakur, Alberto Boschetti, Luca Massaron, Alexey...
TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects.TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. You'll learn how to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing this, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation and use reinforcement learning techniques to play games.By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently.
Oluwole Fagbohun
The TensorFlow Developer Certificate Guide is an indispensable resource for machine learning enthusiasts and data professionals seeking to master TensorFlow and validate their skills by earning the certification. This practical guide equips you with the skills and knowledge necessary to build robust deep learning models that effectively tackle real-world challenges across diverse industries.You’ll embark on a journey of skill acquisition through easy-to-follow, step-by-step explanations and practical examples, mastering the craft of building sophisticated models using TensorFlow 2.x and overcoming common hurdles such as overfitting and data augmentation. With this book, you’ll discover a wide range of practical applications, including computer vision, natural language processing, and time series prediction.To prepare you for the TensorFlow Developer Certificate exam, it offers comprehensive coverage of exam topics, including image classification, natural language processing (NLP), and time series analysis. With the TensorFlow certification, you’ll be primed to tackle a broad spectrum of business problems and advance your career in the exciting field of machine learning. Whether you are a novice or an experienced developer, this guide will propel you to achieve your aspirations and become a highly skilled TensorFlow professional.
Nick McClure
TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow.This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.
Nick McClure
TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow.This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.
Nick McClure
TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before. With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production.By the end of the book, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios.
Ankit Jain, Armando Fandango, Amita Kapoor
TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem.To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification.As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts.By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work.
Md. Rezaul Karim
Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google’s brainchild, is immensely popular and extensively used for predictive analysis.This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features. This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. This book is repurposed for this specific learning experience from material from Packt's Predictive Analytics with TensorFlow by Md. Rezaul Karim.
Kaushik Balakrishnan
Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving.The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator.By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems.