More Pockets Please


Book Description

Ken ploughed through the book of Job looking for answers but found only questions. He knew his loss was small compared to the loss of many but his pain was just as deep. A one in a million medical condition had left him paralysed and his life in turmoil. There was no coping manual he could turn to eitherKen would have to write his own. He had entered a dark place not knowing how he would come through it or whether looking into the mirror of his soul would reveal a complete stranger looking back at him. Ken had a happy childhood. He played competitive sports, went to church, and believed that learning from past failures could ultimately lead to success. He was excited by life and pressing on to the prize, whether it be a match won, an exam passed, or a girl he liked going out with him. But all too soon it ended, hope turned to despair, and loss of identity led to isolation. But what followed was a time of restoration, of self-discovery, of letting go and accepting that he was a small part of a much bigger picture. So he laughed when others cried, he got up when he fell, and soon realized that a more complete person could be emerging from the dark place than the one who had gone into it. He found strength to press on from loved ones, but more importantly he found the faithfulness of God.
















The Key in the Satin Pocket


Book Description

When Nancy tries on an old brocade jacket in a vintage clothing store with Bess and George, she finds an old safe-deposit box receipt in the pocket and a key sewn into the lining. Soon the girls are tracing items from an old estate scattered in antiques shops across town—and are immersed in a mystery involving long-lost relatives, a missing will, and a hidden fortune.




The Most Complete Food Counter


Book Description

An ultimate and timely companion to the wealth of current news on the link between food content and health, by the bestselling authors of "The Fat Counter".




C# 6.0 Pocket Reference


Book Description

When you need answers for programming with C# 6.0, this practical and tightly focused book tells you exactly what you need to know—without long introductions or bloated samples. Easy to browse, it’s ideal as a quick reference or as a guide to get you rapidly up to speed if you already know Java, C++, or an earlier version of C#. Written by the author of C# 6.0 in a Nutshell, this book covers the entire C# 6.0 language, including: All of C#’s fundamentals Advanced topics such as operator overloading, type constraints, covariance and contravariance, iterators, nullable types, operator lifting, lambda expressions, and closures LINQ, starting with sequences, lazy execution and standard query operators, and finishing with a complete reference to query expressions Dynamic binding and asynchronous functions Unsafe code & pointers, custom attributes, preprocessor directives, and XML documentation




Jersey Coast Refuges


Book Description




Machine Learning Pocket Reference


Book Description

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines