First published in 1982, William Tang’s Spectrum Machine Language for the Absolute Beginner is generally considered to be the best introduction to 8-bit machine code programming ever written. With many great game writers crediting this as the book that got them started, there still is no better way to learn the language at the heart of the ZX Spectrum. * * * As the original publisher Melbourne House wrote: If you are frustrated by the limitations of BASIC and want to write faster, more powerful, space-saving programs or subroutines, Spectrum Machine Language for the Absolute Beginner is the book for you. Even with no previous experience of computer languages, you will be able to discover the ease and power of the Spectrum's own language. Each chapter includes specific examples of machine language applications which can be demonstrated and used on your Spectrum as well as a self-test questionnaire. At the end of the book, all this is brought together in an entire machine language program - from design right through to the complete listing of an exciting, original arcade game. * * * Acorn Books is proud to present its Retro Reproduction Series, a collection of classic computing works from the 1980s and 90s, lovingly reproduced in the 21st century. From standards of programming reference no self-respecting microcomputer programmer would be without, to obscure works not found in print anywhere else, these modern reprints are perfect for any connoisseur of retro computing.

Provides Programming Techniques With Step-by-Step Explanations for the Commodore 64, Atari, Vic 20, Pet-CBM & Apple Computers

0805311912B04062001

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. - Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems - Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics - Features self-contained chapters with a thorough literature review - Assesses the development of future machine learning techniques and the further application of existing techniques