MIPS Assembly Language Programming by Robert Britton
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
109 Pages
Introducing ARM assembly language
This page covers the following topics related to ARM assembly language :ISA varieties, ARM assembly basics, A simple program: Adding numbers, Another example: Hailstone sequence, Another example: Adding digits, Summary of instructions so far, Condition codes, Basic memory instructions, Addressing modes, Initializing memory, Multiple-register memory instructions.
Author(s): Carl Burch, Hendrix College
NA Pages
Assembly Language Programming by Richard Pawson
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.
Author(s): Richard Pawson, Peter Higginson
79 Pages
Lecture Notes in Assembly Language
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.
Author(s): Piotr Fulma
252 Pages
Assembly Language Princeton University
The contents include: High Level Languages, Machine Languages, Assembly Languages, Why Learn Assembly Language, Why Learn ARM Assembly Lang, Von Neumann Architecture, Registers and RAM, ALU, Instruction Format, Signed vs Unsigned, 32-bit Arithmetic, 8- and 16-bit Arithmetic, Loads and Stores, Defining Data, Byte Order.
Author(s): Princeton University
57 Pages
Machine Learning Practice Lectures
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.
Author(s): Andrew H. Fagg
NA Pages
