MSX DATA BOOK, includes originals 1984 specifications of the MSX standard computers.Hardware Specification, covers the MSX standard hardware configuration in terms of the requirements for the LSis,memory size, interrupts, screen, keyboard, and sound used in the main unit; and the various (cassette, floppy, printer, serial, and slot) interfaces and connectors. It also covers topics such as cartridges, expansion, ports, and memory maps.It contains a reference guide for MSXBASIC and information for advanced programming, contains a user's guide to MSX-DOS and Disk BASIC. The book covers the serial (RS-232C) expansion and BIOS calls available in the extended version. Also a SOFTWARE DEVELOPMENT GUIDE, contains information for software developers, and more...

It's hard to imagine, but at the beginning of the eighties, the PC had yet to be invented. If you wanted to create software or play video games, you had to buy a machine called a 'home computer'. Unfortunately, each manufacturer released its own hardware, with its own (incompatible) software. Microsoft and ASCII Corporation Japan wanted to do something about this and invented a standard called MSX (which is short for Microsoft eXtended or Machines with Software eXchangeability - depending on who you ask). The specificationsdescribed a set of minimal software and hardware a machine should have in order to be considered MSX compatible. The ColecoVision, Sega SG-1000 video game system and the Spectravideo SV-318/328 were used as a source of inspiration. The first MSX compatible computer was officially released in 1983. Almost every well known electronics manufacturer jumped on the bandwagon and put its own machines on the market, including Sony, Philips, Yamaha, Pioneer and Sanyo, among many others. However, most companies stayed out of the USA, where the Commodore 64 was dominating at that time.The MSX spawned four generations: MSX, MSX2 (1985), MSX2+ (1988), and MSX turboR (1990). Each version of the MSX standard was downwards compatible with the previous one, but added a faster processor or better graphical capabilities. Using the MSX cartridge system, manufacturers could add modems, MIDI interfaces, touch tablets or sound cards. Several artists composed their music using an MSX at that time. A Sony MSX2 was used as a broadcast video workstation on board at the MIR space station. In total, about over 5 million MSX computers were sold in Japan alone. While not as much as the Commodore 64, this was far more than most other home computers available at that time. However, the MSX never became a worldwide standard, mainlybecause the machines arrived too late in an already saturated market. The MSX was one of the major platforms on which big Japanese game studios, such as Konami, released their games. Some of Konami's most popular titles debuted on the MSX, and its software is considered to be the highest quality available. In the current retro game business, Konami's MSX games are on many people's most wanted list. Rare cartridges, like the Word Processing Unit, are sold for more than $1,500, making the MSX one of the most valuable retro computers on eBay. This book provides an overview of all the hardware and software released by Konami on the MSX, including comparisons, screenshots, tips, tricks, facts and figures.

This book tells the story of the classic home computers that paved the way for the PCs we use today - from 1977s pioneering MITS Altair to the latest swivel screen designs of the iMac and the Tablet PC.

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.