Assignment on 32 bit and 64 bit components in Processor to Application stack

Due to back word compatibility with 32 bit component like OS , application is allowed to run on 64 bit component, but vice versa is not allowed.

Inner component can be 32 or 64 bit if outer is 32

But inner component must be 64 if outer is 64 bit

Any level of miss match will result in problem.

Not applicable for device driver. Cannot run 32 bit driver on 64 bit machine (as per backward compatibility rule).

How to check whether binary is 32bit or 64 bit?

# file <filename>

[root@localhost driver]# file a.out

a.out: ELF 32-bit LSB executable, Intel 80386, version 1 (SYSV), dynamically linked (uses shared libs), for GNU/Linux 2.6.32, BuildID[sha1]=740131a4dacce9424b1db8fc53406040406d41bd, not stripped

How to check Whether OS  is 32 or 64 bit?

#uname –a or uname -m

[root@localhost ~]$ uname -a

Linux localhost.localdomain 3.15.10-201.fc20.i686 #1 SMP Wed Aug 27 21:33:30 UTC 2014 i686 i686 i386 GNU/Linux

[root@localhost ~]$ uname -m

i686

Its 32 bit. If you have a 64-bit OS, instead of i686, you have x86_64 or ia64 in the output of uname –a.

Check machine is 32 or 64 bit:

Use grep flags /proc/cpuinfo or lscpu

[root@localhost ~]$ grep flags /proc/cpuinfo

flags                       : fpu vme de pse tsc msr mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 rdtscp constant_tsc pni monitor ssse3

If this content lm flag then it is 64 bit.

 [root@localhost ~]$ lscpu

Architecture:          i686

CPU op-mode(s):        32-bit

Machine Learning - Basics and classification

• Machine Learning is giving ability to machines to learn/program on it own.
• Machine learning is not restricted to Artificial Intelligence. It is having many applications in various filed, Recommendations System raging from data mining, rating system to automation of house hold activities.
• Machine is said to be 'learning', if its performance improves with experience. 

Classification of Machine Learning (ML):

1. Supervised ML.
2. Unsupervised ML.

1. Supervised ML

• Known Sample data sets are given. (Test Passed / Failed )
• Regression : Continuous value .
• Classification : Discrete value

2. Unsupervised ML

• Data set don't have any label (like Passed /Failed)  
• Just data set is given and algorithm will divide it in cluster.
• As we are not specifying what data belong to what segment/ cluster its unsupervised learning.
• We have to derive structure from unknown data.