Practical Common Lisp serves as a useful introduction to Common Lisp for folks who are curious about Lisp but maybe not yet curious enough to shell out big bucks for a dead-tree book and a good Common Lisp tutorial for folks who want to get down to real coding right away.
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This book will be useful for those in the scientific community who gather data and seek tools for analyzing and interpreting data. It will be a valuable reference for scientists in a variety of disciplines and can serve as a textbook for a graduate course in exploratory data analysis as well as a supplemental text in courses on research methodology, pattern recognition, image processing, and re-mote sensing.
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This book encourages learning by abstraction from concrete examples, of understanding calculus through actually ‘doing’ it in an explicitly operational manner, and of gaining oversight of the layers between a simple, foundational system and a rich language of variegated constructs and structures.
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The book is centered around three conceptual pieces that are fundamental to operating systems: virtualization, concurrency, and persistence. In understanding the conceptual, you will also learn the practical, including how an operating system does things like schedule the CPU, manage memory, and store files persistently. The title is an homage to one of the greatest sets of lecture notes ever created, by one Richard Feynman on the topic of Physics.
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You’ll begin your exploration in Python by understanding the key differences between Python 3 and the previous versions of the language. From there, you’ll set up a programming environment for your relevant local or server-based system, and begin by learning general Python code structure, syntax, and data types.
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This book is aimed primarily at IT professionals who want to get up to speed quickly on F#. A working knowledge of the .NET Framework and some knowledge of either C# or Visual Basic would be nice, but it’s not necessary.
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Digital Signal Processing is the science of using computers to understand these types of data. This includes a wide variety of goals: filtering, speech recognition, image enhancement, data compression, neural networks, and much more. DSP is one of the most powerful technologies that will shape science and engineering in the twenty-first century.
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Demonstrating student learning may seem like a simple task, but reflection reveals that it is a complex challenge requiring educators to use specific knowledge and skills. Standards-based reform has many curricular and instructional prerequisites.
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