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Machine Learning for Finance: Principles and practice for financial insiders
Machine Learning for Finance: Principles and practice for financial insiders

A guide to advances in machine learning for financial professionals, with working Python code

Key Features

  • Explore advances in machine learning and how to put them to work in financial industries
  • Clear explanation and expert discussion of how machine learning works, with...
Java 11 and 12 – New Features: Learn about Project Amber and the latest developments in the Java language and platform
Java 11 and 12 – New Features: Learn about Project Amber and the latest developments in the Java language and platform

Enhance your development skills with Java's state-of-the-art features and projects to make your applications leaner and faster

Key Features

  • Overcome the challenges involved in migrating to new versions of Java
  • Discover how Oracle has bridged the gap between Java and...
Practical Big Data Analytics: Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R
Practical Big Data Analytics: Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R

Get command of your organizational Big Data using the power of data science and analytics

Key Features

  • A perfect companion to boost your Big Data storing, processing, analyzing skills to help you take informed business decisions
  • Work with the best tools such as Apache...
Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem
Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem

Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn

Key Features

  • Build a variety of Hidden Markov Models (HMM)
  • Create and apply models to any sequence of data to analyze, predict, and extract valuable...
Linguistic Structure Prediction (Synthesis Lectures on Human Language Technologies)
Linguistic Structure Prediction (Synthesis Lectures on Human Language Technologies)

A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or...

Pro TypeScript: Application-Scale JavaScript Development
Pro TypeScript: Application-Scale JavaScript Development

Explore the features of this innovative open source language in depth, from working with the type system through object-orientation to understanding the runtime and the TypeScript compiler. This fully revised and updated second edition of Steve Fenton’s popular book covers everything you need to discover this fascinating...

Information Theory and Statistics: A Tutorial
Information Theory and Statistics: A Tutorial
Information Theory and Statistics: A Tutorial is concerned with applications of information theory concepts in statistics, in the finite alphabet setting. The topics covered include large deviations, hypothesis testing, maximum likelihood estimation in exponential families, analysis of contingency tables, and iterative algorithms with an...
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource...

Understanding Computational Bayesian Statistics (Wiley Series in Computational Statistics)
Understanding Computational Bayesian Statistics (Wiley Series in Computational Statistics)
In theory, Bayesian statistics is very simple. The posterior is proportional to the prior times likelihood. This gives the shape of the posterior, but it is not a density so it cannot be used for inference. The exact scale factor needed to make this a density can be found only in a few special cases. For other cases, the scale...
A Computational Model of Natural Language Communication: Interpretation, Inference, and Production in Database Semantics
A Computational Model of Natural Language Communication: Interpretation, Inference, and Production in Database Semantics
Everyday life would be easier if we could simply talk with machines instead of having to program them. Before such talking robots can be built, however, there must be a theory of how communicating with natural language works. This requires not only a grammatical analysis of the language signs, but also a model of the cognitive agent, with...
Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering (Water Science and Technology Library)
Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering (Water Science and Technology Library)

“Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important...

Probability and Statistics for Computer Scientists
Probability and Statistics for Computer Scientists

Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling Tools
Incorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic
...

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