math_test
How To Code The Optimization Process
Stochastic Gradient Descent:
The Training Loop From Scratch
View Case Study
Prefer using email? Say hi at kle.tobias@googlemail.com
LOVE-LETTER-FOR-YOU.TXT.vbs might not be what it seems.
This is the portfolio website of Tobias Klein. Join me, as I document my endeavours in the realm of Linux, Data Sciene and much more! Get In Touch
How To Code The Optimization Process
Stochastic Gradient Descent:
The Training Loop From Scratch
View Case Study
How To Code The Optimization Process
Stochastic Gradient Descent:
The Training Loop From Scratch
View Case Study
The README.md File! Read it.
The README.md file gives information about posts I wrote and has links to all of them. There are links to projects, which are in pdf format as well. It describes the contents of each post briefly, in order to make it easier for the reader to find what he or she is looking for. View Case Study
Combining pandas, pyjanitor and Regular Expressions To Get The Job Done.
More Efficient Data Cleaning By Using The pyjanitor Module and Method Chaining View Case Study
Combining pandas, pyjanitor and Regular Expressions To Get The Job Done.
More Efficient Data Cleaning By Using The pyjanitor Module and Method Chaining View Case Study
Combining pandas, pyjanitor and Regular Expressions To Get The Job Done.
More Efficient Data Cleaning By Using The pyjanitor Module and Method Chaining View Case Study
Data Preparation Series: Exploring Tabular Data With pandas: An Overview Of Available Tools In The pandas Library
An Overview Of Available Tools In The Pandas Library View Case Study
The systemctl command is introduced in this section, as found in CentOS 7 / Red Hat 7. It describes and gives multiple examples of how it can be used. View Case Study
This section introduces and explains the terms Application, Script, Process, Daemon, Thread and Job. These will be described in more detail in the following sections. View Case Study
Sections 10 and 11 describe popular directory services and system utility commands. View Case Study
Section 8 explores how a system administrator can communicate with users, by use of native command line tools. The following Section 9 is all about Linux Account Authentication. View Case Study
In this section, we look at how to monitor users, as a system administrator. We will cover `who`, `last`, `w`, `finger` and the `id` command. View Case Study
The `sudo` command is explored in this section along with how to switch users, using the command line. Only basic commands are used, that are available in any Linux distribution and Unix based system. View Case Study
The `/etc/login.defs` file is described and the variables defined inside this file in terms of their definition and usage when running the `useradd` command. View Case Study
In Chapter 5 Section 4, we take a look at some important commands used for the user account management, as often done by system administrators in a corporate environment. View Case Study
This section covers some of the essential commands and concepts used in any Linux distribution. View Case Study
In Chapter 5 Section 1 & 2, we take a look at several Linux text editors and compare the vi text editor to the vim text editor. Plus, we give an introduction to how to use the vi/vim text editors. View Case Study
High-fidelity mobile app designs for a super awesome social media company.
This is a demo post that shows what you can do inside portfolio and blog posts. We’ve included everything you need to create engaging posts and case studies to show off your work in a beautiful way. View Case Study
I adhere to two principals, in that order.
I follow two principles, in this order. A methodically clean and conscientious approach followed by the clear and aesthetically pleasing communication of information. In addition, I do my best to use the available tools efficiently and flexibly. Be it the commands provided by the CentOS (~Red Hat Linux) distribution for system administration, the workflow in Python for reading raw data from .csv files, custom web scraping algorithms or from a database directly, to a production-ready predictive machine learning model that can be deployed via Docker or AWS and serve the client's purposes.
A balance between low deployment costs through virtualization and the use of scalable, on-demand cloud services that keep costs in check, and a competitive advantage for the customer through the use of the finished product.
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