Best laptop for machine learning
Let`s start with the good news - for penetration testing, hacking, any average computer will do.
Installed operating system KALI LINUX, a debian-based distribution, tailored for penetration testing or pentesting, and, in general, the huge range of its capabilities, depending on the goals of the user.
I'm starting another thread of choosing Best laptop for machine learning. You need a laptop with a well-supported by some normal driver (from here) wifi (monitor mode is required). Needed for Gentoo and software development (Eclipse, PyCharm, etc.), should pull a VirtualBox virtual machine (well, just test something on another platform, be it android or stock Windows). I prefer small diagonals of the screen, which, by the way, is definitely needed matte.
Network equipment / Wi-Fi adapter
To access the Internet fit almost any network adapter. Even when testing web applications, we rarely run into network card bandwidth limitations. Most often, slowdowns are related to the response speed of the tested server/website, DoS/anti-brute force protection, lack of RAM on the pentester's computer, insufficient Internet bandwidth, etc. But never have I encountered insufficient bandwidth on my network card.
If you are going to hack Wi-Fi, then not all wireless network cards are suitable. If this is important to you, then the details are here.
Only when using one program did I encounter a lack of RAM. This program is IVRE. For most other situations, the RAM of an average and even low-power computer should be enough to run almost any application in a single thread.
If you plan to use the OS for penetration testing in a virtual machine, then in this situation it is better to take care of a sufficient amount of RAM.
RAM requirements for virtual machines:
- Arch Linux GUI - 2 GB of RAM for a very comfortable work experience
- Kali Linux with GUI - 2 GB of RAM for normal operation
- Kali Linux with GUI - 3-4 gigabytes of RAM for a very comfortable work
Any Linux without a graphical interface - about 100 megabytes for the operation of the system itself + the amount that the programs you run will consume
- Windows latest versions - 2 GB just to start (a lot of brakes)
- Windows latest versions - 4 or more GB for comfortable work.
For example, I have 8 gigabytes in my main system, I allocated 2 gigabytes of RAM to Arch Linux and Kali Linux, I run them (if necessary) at the same time and work comfortably in them. If you plan to use the OS for pentensing in virtual machines, then I would recommend having at least 8 gigabytes - this is enough to comfortably run one or two systems, and most programs are in these systems.
Nevertheless, if you plan to run many programs (or one program in many threads), or if you want to build a virtual network from several virtual machines, then 16 gigabytes will not be superfluous (I plan to increase it to 16 gigabytes on my laptop, fortunately there is two empty slots).
Anything over 16 gigabytes of RAM is unlikely to ever come in handy when pentesting.
If you are going to enumerate hashes and do it with the help of the central processor and not the graphics card, then the more powerful the processor, the faster the enumeration will go. Also, a powerful processor with a large number of cores will allow you to work in virtual machines with great comfort (I allocate 2 cores to each virtual machine with a graphical interface).
The vast majority of programs (except those that sort through hashes) are not demanding on processor power.
There are no special requirements. Naturally, it is more pleasant to work with SSD.
The most common type of machine learning algorithm used today is called linear regression. Linear regression is a statistical technique used to analyze relationships between two variables; one variable is the input (the independent variable) and another is the output (the dependent variable). In practical applications, the dependent variable is often a continuous value such as a number or price. For example, the cost of a product may increase as the size increases. Sometimes, however, the dependent variable is categorical, such as gender.
Which processor is good for machine learning?
The best core processor for machine learning is either an Intel Core i7 or higher or an AMD Ryzen. An Intel Core processor gives you processing speed and performance but the parallel processing capability in AMD Ryzen ensures you can tackle multiple computational or design tasks at the same time.