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Scala: Guide for Data Science Professionals
Scala will be a valuable tool to have on hand during your data science journey for everything from data cleaning to cutting-edge machine learning About This Book Build data science and data engineering solutions with ease An in-depth look at each stage of the data analysis process — from reading and collecting data to distributed analytics Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulations, and source code Who This Book Is For This learning path is perfect for those who are comfortable with Scala programming and now want to enter the field of data science. Some knowledge of statistics is expected. What You Will L...
Scala:Applied Machine Learning
Leverage the power of Scala and master the art of building, improving, and validating scalable machine learning and AI applications using Scala's most advanced and finest features About This Book Build functional, type-safe routines to interact with relational and NoSQL databases with the help of the tutorials and examples provided Leverage your expertise in Scala programming to create and customize your own scalable machine learning algorithms Experiment with different techniques; evaluate their benefits and limitations using real-world financial applications Get to know the best practices to incorporate new Big Data machine learning in your data-driven enterprise and gain future scalabilit...
Getting Started with Julia
This book is for you if you are a data scientist or working on any technical or scientific computation projects. The book assumes you have a basic working knowledge of high-level dynamic languages such as MATLAB, R, Python, or Ruby.
Classical Statistical Mechanics with Nested Sampling
This thesis develops a nested sampling algorithm into a black box tool for directly calculating the partition function, and thus the complete phase diagram of a material, from the interatomic potential energy function. It represents a significant step forward in our ability to accurately describe the finite temperature properties of materials. In principle, the macroscopic phases of matter are related to the microscopic interactions of atoms by statistical mechanics and the partition function. In practice, direct calculation of the partition function has proved infeasible for realistic models of atomic interactions, even with modern atomistic simulation methods. The thesis also shows how the output of nested sampling calculations can be processed to calculate the complete PVT (pressure–volume–temperature) equation of state for a material, and applies the nested sampling algorithm to calculate the pressure–temperature phase diagrams of aluminium and a model binary alloy.
Large Scale Machine Learning with Python
Learn to build powerful machine learning models quickly and deploy large-scale predictive applications About This Book Design, engineer and deploy scalable machine learning solutions with the power of Python Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful. What You Will Learn Ap...
Scala for Data Science
Leverage the power of Scala with different tools to build scalable, robust data science applications About This Book A complete guide for scalable data science solutions, from data ingestion to data visualization Deploy horizontally scalable data processing pipelines and take advantage of web frameworks to build engaging visualizations Build functional, type-safe routines to interact with relational and NoSQL databases with the help of tutorials and examples provided Who This Book Is For If you are a Scala developer or data scientist, or if you want to enter the field of data science, then this book will give you all the tools you need to implement data science solutions. What You Will Learn...
Tropical Forest Ecology and Management for the Anthropocene
This Special Issue looks forward as well as backward to best analyze the forest conservation challenges of the Caribbean. This is made possible by 75 years of research and applications by the United States Department of Agriculture, International Institute of Tropical Forestry (the Institute) of Puerto Rico. It transforms Holocene-based scientific paradigms of the tropics into Anthropocene applications and outlooks of wilderness, managed forests, and urban environments. This volume showcases how the focus of the Institute’s programs is evolving to support sustainable tropical forest conservation despite uncertain conditions. The manuscripts showcased here highlight the importance of shared...
Blaise Pascal
Blaise Pascal (1623-1662), mathematician, physicist, inventor, and religious thinker was a man at odds with his time. The optimism of the Enlightenment and the belief among philosophers and scientists that the universe was both discoverable and rational made them feel invincible. Reason alone, declared the intellectuals, could discover a God of natural religion that was to replace the God of traditional Christianity. Pascal, on the other hand, was not so confident. In his Pens es, he wrote, "The eternal silence of these infinite spaces fills me with dread." For Pascal, the universe was full of a mystery that went far beyond the powers of reason. Blaise Pascal: Reasons of the Heart, the lates...
Julia: High Performance Programming
Leverage the power of Julia to design and develop high performing programs About This Book Get to know the best techniques to create blazingly fast programs with Julia Stand out from the crowd by developing code that runs faster than your peers' code Complete an extensive data science project through the entire cycle from ETL to analytics and data visualization Who This Book Is For This learning path is for data scientists and for all those who work in technical and scientific computation projects. It will be great for Julia developers who are interested in high-performance technical computing. This learning path assumes that you already have some basic working knowledge of Julia's syntax an...
