“…From the data you collect, you will be able to calculate the statistics and create R plots to visualize them. Finally, you will discover how to use Selenium drivers with R for more sophisticated scraping. …”

“…What You Will Learn Understand the internals of a Hibernate session and how Entities are managed Declare better mapping between entity classes and database tables Manage entity associations and collections Fetch data not just by entity ID, but also using HQL, Criteria Objects, Filters, and Native SQL Observe the first and second level caches and find out how to manage them Collect statistics and metrics data for further observation Make your application work with multi-tenant databases In Detail Hibernate has been so successful since its inception that it even influenced the Java Enterprise Edition specification in that the Java Persistence API was dramatically changed to do it the Hibernate way. …”

“…At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. …”

“…It includes the following advanced functions: Policy management to control physical volume pooling Cache management Redundant copies, including across a grid network Copy mode control The IBM TS7700 offers enhanced statistical reporting. It also includes a standards-based Management Interface (MI) for IBM TS7700 management. …”

“…The second edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. …”

“…What you will learn Understand the fundamental nature and workflow of predictive data modeling Explore how different types of visual and linguistic signals are processed by neural networks Dive into the mathematical and statistical ideas behind how networks learn from data Design and implement various neural networks such as CNNs, LSTMs, and GANs Use different architectures to tackle cognitive tasks and embed intelligence in systems Learn how to generate synthetic data and use augmentation strategies to improve your models Stay on top of the latest academic and commercial developments in the field of AI Who this book is for This book is for machine learning practitioners, deep learning researchers and AI enthusiasts who are looking to get well versed with different neural network architecture using Keras. …”

“…This Tableau book will introduce you to visual statistical analytics capabilities, create different types of visualizations and dynamic dashboards for rich user experiences. …”

“…The book assumes college-level knowledge of math and statistics. Downloading the example code for this ebook: You can download the example code files for this ebook on GitHub at the following link: https://github.com/PacktPublishing/Machine-Learning-for-Finance . …”

“…Get a high-level exposure to why statistics and figures may mislead as well as enlighten. …”

“…Some prior knowledge of machine learning concepts and statistics is desirable…”

“…You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. …”

“…We wrap up the analytics portion of the book with the application of statistical analysis, complex event processing, and deep learning models. …”

“…For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. …”

“…To make the best use of this book, a background in linear algebra, differential equations, basics of statistics, data types, data structures, and numerical algorithms will be useful…”

“…Cheng & Cirillo address the problem of modeling the dependency between PD and LGD using an original, urn-based statistical model. Fadina & Schmidt propose an improvement of intensity-based default models by accounting for ambiguity around both the intensity process and the recovery rate. …”

“…Significant progress has been achieved in this respect, for example in the development of objective (operator-independent), local segmentation techniques adapted for X-ray μCT images, in terms of improvements of hybridisation (FISH) technologies to locate bacterial and archaeal cells in soil thin sections, or in the in elaboration of statistical tools to interpolate 2-D measurements to produce 3-D data. …”

“…What You Will Learn Review fundamental concepts such as bias and variance Extract features from categorical variables, text, and images Predict the values of continuous variables using linear regression and K Nearest Neighbors Classify documents and images using logistic regression and support vector machines Create ensembles of estimators using bagging and boosting techniques Discover hidden structures in data using K-Means clustering Evaluate the performance of machine learning systems in common tasks In Detail Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. …”

“…Later, you'll learn how quantitative trading signals and strategies are developed, and also implement and analyze sophisticated trading strategies such as volatility strategies, economic release strategies, and statistical arbitrage. Finally, you'll create a trading bot from scratch using the algorithms built in the previous sections. …”

“…By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.What you will learnFind out how to use decomposition to solve problems through visual representationEmploy pattern generalization and abstraction to design solutionsBuild analytical skills required to assess algorithmic solutionsUse computational thinking with Python for statistical analysisUnderstand the input and output needs for designing algorithmic solutionsUse computational thinking to solve data processing problemsIdentify errors in logical processing to refine your solution designApply computational thinking in various domains, such as cryptography, economics, and machine learningWho this book is forThis book is for students, developers, and professionals looking to develop problem-solving skills and tactics involved in writing or debugging software programs and applications. …”

“…Miller’s modeling techniques draw on methods from economics, accounting, finance, classical and Bayesian statistics, machine learning, simulation, and mathematical programming. …”