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.
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 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.
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: 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.
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.