Big data
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, Dr. 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.
Teradata Cookbook. Over 85 recipes to implement efficient data warehousing solutions
Abhinav Khandelwal, Rajsekhar Bhamidipati
Teradata is an enterprise software company that develops and sells its eponymous relational database management system (RDBMS), which is considered to be a leading data warehousing solutions and provides data management solutions for analytics. This book will help you get all the practical information you need for the creation and implementation of your data warehousing solution using Teradata.The book begins with recipes on quickly setting up a development environment so you can work with different types of data structuring and manipulation function. You will tackle all problems related to efficient querying, stored procedure searching, and navigation techniques. Additionally, you’ll master various administrative tasks such as user and security management, workload management, high availability, performance tuning, and monitoring.This book is designed to take you through the best practices of performing the real daily tasks of a Teradata DBA, and will help you tackle any problem you might encounter in the process.
Justin Bozonier
Machine learning is the process of teaching machines to remember data patterns, using them to predict future outcomes, and offering choices that would appeal to individuals based on their past preferences.Machine learning is applicable to a lot of what you do every day. As a result, you can’t take forever to deliver your first iteration of software. Learning to build machine learning algorithms within a controlled test framework will speed up your time to deliver, quantify quality expectations with your clients, and enable rapid iteration and collaboration.This book will show you how to quantifiably test machine learning algorithms. The very different, foundational approach of this book starts every example algorithm with the simplest thing that could possibly work. With this approach, seasoned veterans will find simpler approaches to beginning a machine learning algorithm. You will learn how to iterate on these algorithms to enable rapid delivery and improve performance expectations.The book begins with an introduction to test driving machine learning and quantifying model quality. From there, you will test a neural network, predict values with regression, and build upon regression techniques with logistic regression. You will discover how to test different approaches to naïve bayes and compare them quantitatively, along with how to apply OOP (Object-Oriented Programming) and OOP patterns to test-driven code, leveraging SciKit-Learn.Finally, you will walk through the development of an algorithm which maximizes the expected value of profit for a marketing campaign by combining one of the classifiers covered with the multiple regression example in the book.
Mercury Learning and Information, Andres Fortino
This book leverages advanced techniques and tools in data science to extend data analysis from numeric and categorical data to textual data. Designed for business analysts, it uses a case study approach to teach skills in extracting insights from text data, supporting business decision-making. Exercises primarily use Excel and R, covering techniques from basic text analytics to sophisticated methods like topic extraction and text similarity scoring.The course begins with framing analytical questions and exploring analytical tool sets. It progresses through preparing data files, performing word frequency and keyword analysis, and conducting sentiment analysis. Advanced topics include visualizing text data, coding, named entity recognition, and topic recognition in documents. The book also covers text similarity scoring and the analysis of large datasets by sampling.Throughout this journey, readers will apply the CRISP-DM data mining standard, using companion files with numerous datasets for practical exercises. By the end, participants will have a comprehensive understanding of text analytics, enabling them to derive meaningful insights from textual data to inform business strategies.
MrExcel's Holy Macro! Books, Kasper de Jonge
This book empowers readers to build professional dashboards and reports using Power BI. Starting with the basics of dashboards and reports, it delves into preparing data for visualizations, crafting detailed reports, and designing cohesive dashboards. The book provides clear methods for sharing work efficiently within organizations, ensuring that readers understand how to convert raw data into actionable insights.Practical examples and techniques throughout the book equip professionals with the skills they need to enhance their business intelligence capabilities. Readers will discover how to structure data for clarity, develop key reporting techniques, and integrate advanced features to maximize Power BI's potential.Whether you're a beginner or looking to refine your skills, this book offers a step-by-step approach to mastering Power BI's core capabilities, ensuring a seamless transition from data to decision-making.