Romeo Kienzler takes the reader on a big and detailed tour through significant Spark topics and exercises, which occur in the practical usage of Spark in Big Data, Analytics, Data Science and Analytic Data Warehouse ("ADW") projects. In his book topics like the new Spark V2 Ecosystem, Machine Learning, Spark Streaming, Graph Processing, Cluster Design and Management (Yarn and Mesos), Cloud based deployments, Performance topics around HDFS, Date importing and handling, Spark Data Source API, Spark Dataframes and Datasets API, Code Generation for expression evaluation, Project Tungsten, Spark error handling and much more are covered. If you have taken one or more of the well done Spark courses from Databricks before, the topics might familiar but the book covers even some more enhanced topics as well it can be taken as a good comprehension or as in-depth notes. Additionally the book focus on very specific details and problems in parallel programming with Spark, derived from practical use cases.As well the book contains links and references on papers, literature and web forums. To summarize I would recommend this book as an excellent starting point and Spark reference guide.
"PREDICTION IS VERY difficult, especially if it's about the future," said Physics Nobel Laureate Niels Bohr. Bohr was presumably talking about the vagaries of quantum mechanical subatomic life, but the statement holds true at other scales too. Predicting the future is tough, and any good scientist knows enough to hedge his or her bets. That's what error bars are all about. It's why science usually proceeds methodically: hypotheses are formulated, experiments conducted, observations collated, and data evaluated.
I would recommend that you start with Introduction to Statistical Learning with R (usually shortened as ISLR). A lot of people have adapted the examples to Python if you google a bit and it's an excellent book that hides just enough complexity to not be overwhelming. Plus, once you have a good understanding of all of it, you can either graduate to the more extensive version (Elements of Statistical Learning, usually shortened as ESL) for a more rigorous treatment of the same thing, or choose to go for something different like Bishop's Pattern Recognition and Machine Learning. ISLR is free as a pdf and has a corresponding MOOC. ESL doesn't, but is also free on the author's website.
The book python machine learning, second edition by Sebastian Raschka and Vahid Mirjalili is a tutorial to a broad range of machine learning applications with Python. It provides a practical introduction to machine learning using popular libraries like SciPy, NumPy, scikit-learn, Matplotlib, and pandas. The main revision to the first edition is more chapters on neural network practices. There are now five chapters that discuss neural networks, and their implementation in TensorFlow. Besides the additional content, a lot of concepts of the first edition are refined.
This Jerry Kaplan classic, written in 2015 has been chosen as one of the 10 best science and innovation books of 2015 by The Economist. In the book, Kaplan discusses the most recent advances in robotic automation technology, machine learning, and perception powering systems that rival or surpass human abilities. A Martin Ford non-fiction, written in 2015, this book takes a scary voyage through Artificial Intelligence's quick advances. In his book, Bostrom talks of how if machine intelligence outperformed human intelligence, at that point this new superintelligence can be greatly effective.
"In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of'pattern recognition' or'machine learning'. "Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. "Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. "This accessible monograph seeks to provide a comprehensive introduction to the fields of pattern recognition and machine learning.
Many people, especially the long-term practitioners in humanities and similar disciplines, find this change worrying, and in many ways exactly contrary to the spirit of these disciplines. However, the aim of this book is neither to teach R or programming, but to give the Literature students just the most basic tools needed to do some relatively straightforward textual analysis. The book takes the freely available text file of "Moby Dick" and runs a variety of textual analysis on it: simple word count and word frequencies, correlations between various "special" words, context analysis, etc. Even though this is primarily a book intended for literature students, I would actually strongly recommend it to anyone interested in text mining, text analysis and natural language processing.
I have hundreds of papers and books on Neural Nets from the time of Rosenblatt's Perceptron on through autoencoders, recurrent NNs, convolutional NNs, RBM's, DNN's, greedy pretraining, Kolmogrov's universal approximation theorem, optimization methods for weight training, and more. I found this book to provide a conceptual overview of the DNNs and the architectures (feed forward, deep belief, unsupervised pre-trained, convolutional, recurrent, long and short term memory, and recursive, networks). The book provides the conceptual connective tissue that are the muscles that the practitioner must bond to the architectural bones to move forward in Deep Learning. Every chapter offers new nuggets about how to apply their framework to real world ML problems, and about real world ML problems.
In this new edition, Appendix A (PDF) describes the results of experimental tests since the first edition of the book was published in 1981, and discusses ten new tests. They were then tested three hours later, each chick being exposed sequentially to the control and the test stimulus, when most test birds were averse to pecking the yellow LED, but not averse to pecking the control bead. I will not attempt to answer his polemic, ranging from Nietzsche to ley-lines, but simply start by looking again at his predictions about the chicks: "No secular trends apparent; latencies to peck the illuminated bead after ten weeks are no different from those on week I, and the differencies between latencies for illuminated and chrome beads, if they occur, are also unchanged". In fact secular trends were very apparent, latencies to peck the illuminated bead after ten weeks were very different from those on week I, and the differences between latencies for illuminated and chrome beads were not unchanged.
This is one of the analogies Jimmy Soni and Rob Goodman use to explain information theory in A Mind at Play, the excellent new biography of information age founder Claude Shannon. From a very young age, as the authors explain, Shannon displayed a lively and curious mind. Besides his mathematical theory of communication, during that time, he also laid the groundwork for modern signal processing and important aspects of cryptography (such as the one-time pad). One of the pleasures of modern biography-reading is that when the authors mention Shannon's filmed 1950 demonstration of Theseus at Bell Labs, one can pause to find it on YouTube.