By Patrick Nicolas
- Explore a vast number of facts processing, computer studying, and genetic algorithms via diagrams, mathematical formula, and up to date resource code in Scala
- Take your services in Scala programming to the subsequent point via growing and customizing AI applications
- Experiment with varied recommendations and assessment their merits and obstacles utilizing real-world purposes in an instructional style
The discovery of data via facts clustering and type is turning into a key differentiator for aggressive agencies. desktop studying purposes are far and wide, from self-driving automobiles, engineering layout, logistics, production, and buying and selling innovations, to detection of genetic anomalies.
The e-book is your one cease advisor that introduces you to thefunctional features of the Scala programming language which are severe to the construction of computing device studying algorithms reminiscent of dependency injection and implicits.
You commence through studying information preprocessing and filtering recommendations. Following this, you are going to stream directly to unsupervised studying concepts akin to clustering and measurement relief, by way of probabilistic graphical types corresponding to Naïve Bayes, hidden Markov types and Monte Carlo inference. extra, it covers the discriminative algorithms corresponding to linear, logistic regression with regularization, kernelization, aid vector machines, neural networks, and deep studying. You’ll circulation directly to evolutionary computing, multibandit algorithms, and reinforcement learning.
Finally, the e-book encompasses a entire evaluate of parallel computing in Scala and Akka by means of an outline of Apache Spark and its ML library. With up to date codes in accordance with the newest model of Scala and entire examples, this e-book will make sure that you've greater than only a sturdy basic wisdom in computer studying with Scala.
What you'll learn
- Build dynamic workflows for medical computing
- Leverage open resource libraries to extract styles from time series
- Write your individual category, clustering, or evolutionary algorithm
- Perform relative functionality tuning and evaluate of Spark
- Master probabilistic types for sequential data
- Experiment with complicated strategies equivalent to regularization and kernelization
- Dive into neural networks and a few deep studying architecture
- Apply a few easy multiarm-bandit algorithms
- Solve immense information issues of Scala parallel collections, Akka actors, and Apache Spark clusters
- Apply key studying concepts to a technical research of economic markets
About the Author
Patrick Nicolas is a Director of knowledge engineering at Agile SDE in San Francisco bay region, California. He has 25 years of expertise in software program engineering and development purposes in C++, Java, and extra lately in Scala, and has held numerous managerial positions. His pursuits comprise real-time analytics, modeling and improvement of nonlinear models.
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Key FeaturesExplore a wide number of info processing, computing device studying, and genetic algorithms via diagrams, mathematical formula, and up to date resource code in ScalaTake your services in Scala programming to the subsequent point by means of developing and customizing AI applicationsExperiment with varied options and assessment their advantages and barriers utilizing real-world functions in an educational styleBook DescriptionThe discovery of data via information clustering and category is turning into a key differentiator for aggressive firms.
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Additional info for Scala for Machine Learning - Second Edition
Scala for Machine Learning - Second Edition by Patrick Nicolas
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