Ebooks

Quo vadis

Henryk Sienkiewicz

Quo vadis

Henryk Sienkiewicz

Quo Vadis

Henryk Sienkiewicz

Quo vadis? A Narrative of the Time of Nero

Henryk Sienkiewicz

Quo Vadis. A Narrative of the Time of Nero

Henryk Sienkiewicz

Quo vadis? Henryka Sienkiewicza. Od legendy do arcydzieła

Teresa Świętosławska

Quo vadis, Rosjo? Spojrzenie ambasadora Niemiec

Ruediger von Fritsch

Quodlibet [co się podoba]. Studia dominikańskie

Praca zbiorowa

R Bioinformatics Cookbook. Use R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis

Dan MacLean

R Bioinformatics Cookbook. Utilize R packages for bioinformatics, genomics, data science, and machine learning - Second Edition

Dan MacLean

R: Data Analysis and Visualization. Click here to enter text

Tony Fischetti, Brett Lantz, Jaynal Abedin, Hrishi V. Mittal, ...

R Data Analysis Cookbook. Customizable R Recipes for data mining, data visualization and time series analysis - Second Edition

Kuntal Ganguly, Shanthi Viswanathan, Viswa Viswanathan

R Data Analysis Projects. Build end to end analytics systems to get deeper insights from your data

Gopi Subramanian

R Data Mining. Implement data mining techniques through practical use cases and real-world datasets

Andrea Cirillo

R Data Science Essentials. R Data Science Essentials

Raja B. Koushik, Sharan Kumar Ravindran

R Data Structures and Algorithms. Increase speed and performance of your applications with effi cient data structures and algorithms

PKS Prakash, Achyutuni Sri Krishna Rao

R Data Visualization Cookbook. Over 80 recipes to analyze data and create stunning visualizations with R

Atmajitsinh Gohil, Atmajitsingh Gohil

R Data Visualization Recipes. A cookbook with 65+ data visualization recipes for smarter decision-making

Vitor Bianchi Lanzetta

R Deep Learning Cookbook. Solve complex neural net problems with TensorFlow, H2O and MXNet

PKS Prakash, Achyutuni Sri Krishna Rao

R Deep Learning Essentials. A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet - Second Edition

Mark Hodnett, Joshua F. Wiley

R Deep Learning Projects. Master the techniques to design and develop neural network models in R

Yuxi (Hayden) Liu, Pablo Maldonado

R for Data Science Cookbook. Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques

Yu-Wei, Chiu (David Chiu)

R for Data Science. Learn and explore the fundamentals of data science with R

Dan Toomey

R Graph Essentials

David Lillis, David A Lillis