



Deep Learning with Python: A Handson Introduction
Discover the practical aspects of implementing deeplearning solutions using the rich Python ecosystem. This book bridges the gap between the academic stateoftheart and the industry stateofthepractice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often...   Advanced Analytics with Spark: Patterns for Learning from Data at Scale
In this practical book, four Cloudera data scientists present a set of selfcontained patterns for performing largescale data analysis with Spark. The authors bring Spark, statistical methods, and realworld data sets together to teach you how to approach analytics problems by example.
You’ll start with an...   Blockchain Basics: A NonTechnical Introduction in 25 Steps
In 25 concise steps, you will learn the basics of blockchain technology. No mathematical formulas, program code, or computer science jargon are used. No previous knowledge in computer science, mathematics, programming, or cryptography is required. Terminology is explained through pictures, analogies, and metaphors.
This book... 


Bad to the Bone: Crafting Electronics Systems with Beaglebone and BeagleBone Black
This comprehensive book provides detailed materials for both novice and experienced programmers using all BeagleBone variants which host a powerful 32bit, superscalar TI Sitara ARM Cortex A8 processor. Authored by Steven F. Barrett and Jason Kridner, a seasoned ECE educator along with the founder of Beagleboard.org, respectively, the work...   Reinforcement and Systemic Machine Learning for Decision Making
Reinforcement and Systemic Machine Learning for Decision Making
There are always difficulties in making machines that learn from experience. Complete information is not always available—or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the...   Unsupervised Learning with R
Work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data
About This Book

Unlock and discover how to tackle clusters of raw data through practical examples in R

Explore your data and create your own models from scratch
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