Frank Kane

Frank Kane's Taming Big Data with Apache Spark and Python

Cynthia Ward

Lester del Rey

Laird Long

Hulbert Footner

Richard Wilson

Michael Allan Mallory

Allan Danzig

James H. Beard

Welcome to Black Cat Weekly #23. Lots of good stuff this time—highlighted by a novel from Golden Age mystery author Rufus King, Duenna for a Murder. Plus a few novellas, and lots of great short stories, a solve-it-yourself mystery from Hal Charles, and great selections from Michael Bracken (Laird Long’s “Taken for a Ride”—which qualifies as both a mysery and a fantasy story) and Barb Goffman (Michael Allan Mallory’s “Random Harvest”). On the science fiction side, the Cynthia Ward Presents story is missing this week, but that’s only because we have a fantastic alternate-history story from Cynthia herself! Check out her “On Stony Ground.” Plus an epic disaster story from Allan Danzig, a fantasy from Unknown by Lester del Rey and James H. Beard, a space-based tale by Richard Wilson, and a miniature military SF story from Larry Tritten. Here’s the complete lineup: Mysteries / Suspense / Adventure: “Soul Searching,” by Laird Long [short story] “A Fine Kettle of Fish,” by Hal Charles [Solve-It-Yourself Mystery] “Dead Wrong,” by Frank Kane [short story] “Taken for a Ride,” by Hulbert Footner [short novel] “Random Harvest,” by Michael Allan Mallory [Barb Goffman Presents short story] Duenna to a Murder, by Rufus King [novel] Science Fiction & Fantasy: “On Stony Ground,” by Cynthia Ward [short story] “Corrigan’s Homunculi,” by Larry Tritten [short story] “Carillon of Skulls,” by Lester del Rey and James H. Beard [short story] “Abel Baker Camel,” by Richard Wilson [short story] “The Great Nebraska Sea,” by Allan Danzig [short story]

Black Cat Weekly #23

Hearse Class Male

This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time.

Hands-On Data Science and Python Machine Learning

It started out as a dog of a case—with Johnny Liddell keeping tabs on a drunken movie star for a fat producer. But things picked up when the actor was found dead in a phony auto accident. “Murder and mayhem in abundant measure.” – The New York Times

A Johnny Liddell Mystery

About Face

Frank Kane's hands-on Spark training course, based on his bestselling Taming Big Data with Apache Spark and Python video, now available in a book. Understand and analyze large data sets using Spark on a single system or on a cluster. About This Book Understand how Spark can be distributed across computing clusters Develop and run Spark jobs efficiently using Python A hands-on tutorial by Frank Kane with over 15 real-world examples teaching you Big Data processing with Spark Who This Book Is For If you are a data scientist or data analyst who wants to learn Big Data processing using Apache Spark and Python, this book is for you. If you have some programming experience in Python, and want to learn how to process large amounts of data using Apache Spark, Frank Kane's Taming Big Data with Apache Spark and Python will also help you. What You Will Learn Find out how you can identify Big Data problems as Spark problems Install and run Apache Spark on your computer or on a cluster Analyze large data sets across many CPUs using Spark's Resilient Distributed Datasets Implement machine learning on Spark using the MLlib library Process continuous streams of data in real time using the Spark streaming module Perform complex network analysis using Spark's GraphX library Use Amazon's Elastic MapReduce service to run your Spark jobs on a cluster In Detail Frank Kane's Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you'll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python. Apache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses. Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease. Style and approach Frank Kane's Taming Big Data with Apache Spark and Python is a hands-on tutorial with over 15 real-world examples carefully explained by Frank in a step-by-step manner. The examples vary in complexity, and you can move through them at your own pace.

She was an alcoholic blonde with a load of hot diamonds—a set-up for murder on the rocks. And only private eye Johnny Liddell can crack the case.

Red Hot Ice

She was a gorgeous - and mysterious - girl from New York, who had taken a low-paying job in a small-town night club. When they fished her out of the local river she had nothing on. It didn’t matter. She was past caring. Johnny Liddell cared, though. The girl was his client and it didn’t make sense. Why would she strip, pile her clothes neatly on the pier, and then take the plunge? A waste, Liddell thought mournfully. A great waste. Then he cheered up. Any case that began with a killer and a naked woman was bound to produce more of the same . . .

Green Light for Death

Learn how to build recommender systems from one of Amazon's pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon's personalized product recommendation technologies.You've seen automated recommendations everywhere - on Netflix's home page, on YouTube, and on Amazon as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you'll become very valuable to them.This book is adapted from Frank's popular online course published by Sundog Education, so you can expect lots of visual aids from its slides and a conversational, accessible tone throughout the book. The graphics and scripts from over 300 slides are included, and you'll have access to all of the source code associated with it as well.We'll cover tried and true recommendation algorithms based on neighborhood-based collaborative filtering, and work our way up to more modern techniques including matrix factorization and even deep learning with artificial neural networks. Along the way, you'll learn from Frank's extensive industry experience to understand the real-world challenges you'll encounter when applying these algorithms at large scale and with real-world data.This book is very hands-on; you'll develop your own framework for evaluating and combining many different recommendation algorithms together, and you'll even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people. We'll cover: -Building a recommendation engine-Evaluating recommender systems-Content-based filtering using item attributes-Neighborhood-based collaborative filtering with user-based, item-based, and KNN CF-Model-based methods including matrix factorization and SVD-Applying deep learning, AI, and artificial neural networks to recommendations-Session-based recommendations with recursive neural networks-Scaling to massive data sets with Apache Spark machine learning, Amazon DSSTNE deep learning, and AWS SageMaker with factorization machines-Real-world challenges and solutions with recommender systems-Case studies from YouTube and Netflix-Building hybrid, ensemble recommendersThis comprehensive book takes you all the way from the early days of collaborative filtering, to bleeding-edge applications of deep neural networks and modern machine learning techniques for recommending the best items to every individual user.The coding exercises for this book use the Python programming language. We include an intro to Python if you're new to it, but you'll need some prior programming experience in order to use this book successfully. We also include a short introduction to deep learning, Tensorfow, and Keras if you are new to the field of artificial intelligence, but you'll need to be able to understand new computer algorithms.Dive in, and learn about one of the most interesting and lucrative applications of machine learning and deep learning there is!

Building Recommender Systems with Machine Learning and AI: Help People Discover New Products and Content with Deep Learning, Neural Networks, and Mach

A case of corpses. They piled up so fast that private eye Johnny Liddell figured he was ahead if he found them while they were still warm. It started when he was hired as a baby-sitter to a wildcat. She was blonde and beautiful, and stacked better than a deck of marked cards. And she had a cool $200,000 worth of hot diamonds. There was just one hitch. She used bourbon instead of perfume.

Johnny Liddell was looking forward to his 2:30 a.m. date with Eve Wylie. Eve had thick red hair that fell down over her shoulders like a molten copper cascade, and Eve had a sullen soft-looking mouth that held more than a casual promise for Johnny Liddell. So when he got to her apartment, and found the date was off, it came as an unpleasant shock. Not that it was Eve’s fault. There was very little a girl, with her throat cut from ear to ear, could do about a broken date.

Slay Ride

"P.I. Johnny Liddell tangles with mobsters, molls -- and murder! ""...the [Johnny Liddell] books are classic examples of the hard-boiled private eye formula...[The] books are often distinguished by a gritty, authentic portrayal of New York cops and their methods..."" —St. James Guide to Crime and Mystery Writers"

Grave Danger

A tense, searing novel of treachery, love and intrigue on a private yacht—destination unknown! Three desperate men and two fiery woman who gambles everything they had in a deadly game of winner-take-all. From the swift, sharp pen of Frank Kane, writer of the Johnny Liddell mystery series comes a breathtaking crashing tale of adventure and suspense.

The Conspirators

Trigger Mortis

John Tilsley

Following the collapse of his business and the loss of his home, Frank Kane made a catastrophic decision. In desperation, he agreed to smuggle cocaine out of Venezuela. Almost inevitably, he and his girlfriend, Sam, were caught. The price they paid was a ten-year sentence in the hell of the overcrowded Venezuelan prison system, notorious for corruption and abuse, and rife with weapons and gangs. At one point, Frank was held in the remote El Dorado prison, better known for being the one-time home of Henri Charrière, or Papillon. He witnessed countless murders as gang leaders fought for power, and he had to become as ruthless as his fellow inmates in order to survive. In an attempt to dull the reality of the horrendous conditions, he succumbed to drugs. After enduring years of systematic beatings by the guards and attempts on his life by inmates, Frank suffered more than one breakdown. He lost over four stone and was riddled with disease, but somehow he found the strength within himself to survive and was eventually released in 2004 after serving over seven years of his sentence. During the long walk back from hell, Frank decided to tell his story.

Frank Kane

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