This PDF book covers the following topics related to
Assembly Language : Data Representation, Boolean Algebra, System Organization,
Memory Layout and Access, Variables and Data Structures, The 80x86 Instruction
Set, The UCR Standard Library, MASM: Directives & Pseudo-Opcodes, Arithmetic and
Logical Operations, Control Structures, Procedures and Functions, Procedures:
Advanced Topics, MS-DOS, PC-BIOS, and File I/O, Floating Point Arithmetic,
Strings and Character Sets, Pattern Matching, Interrupts, Traps, and Exceptions,
Resident Programs, Processes, Coroutines, and Concurrency, The PC Keyboard, The
PC Parallel Ports, The PC Serial Ports, The PC Video Display, The PC Game
Adapter, Optimizing Your Program.
Author(s): Institute of Computing, State
University of Campinas
The purpose of
this book is to give the reader a better understanding of how computers really
work at a lower level than in programming languages like Pascal. By gaining a
deeper understanding of how computers work, the reader can often be much more
productive developing software in higher level languages such as C and C++.
Learning to program in assembly language is an excellent way to achieve this
goal.
This PDF book covers
the following topics related to MIPS Assembly Language Programming : The MIPS
Architecture, Pseudocode, Number Systems, PCSpim The MIPS Simulator, Algorithm
Development, Reentrant Functions, Exception Processing, A Pipelined
Implementation, Embedded Processors.
Author(s): Computer Science
Department, California State University, Chico, California
This PDF covers the following topics related to Assembly Language
Programming : Fundamentals of assembly language, Introduction to assembly
language and ARMlite, Countdown, Matchsticks, Hangman, Indirect & Indexed
addressing, The System Stack, and Subroutines, Interrupts, Snake.
The contents include:
Before we begin, First program, NASM syntax, Basic CPU instructions, Debugging with GDB,
First program linked with a C library, FPU, File operations, MMX, SSE, RDTS, Inline assembler,
Introduction,Registers, Memory.
This lecture note
covers the following topics: Server Configuration, Python Overview, Pandas and
Numpy, Classifiers, Regression, Cross-Validation, Logistic Regression, Support
Vector Machines, Decision Trees, Ensemble Methods, Principal Component Analysis,
Embedding Methods, Clustering, Semi-Supervised Learning.