All the introduction into the world of data you need is on this board.
It's all about clean fusion energy. The goal hereby is to produce and widely distribute clean and affordable energy in a safe way. I will try to explain the concept and idea behind this approach. #futuristictechnology #technology #future #AI #fusionenergy #newsolution #cleanenergy
We have looked at machine learning before, deep learning is a subset, where algorithms in artificial neural networks learn from vast amounts of data. Artificial neural networks mimic the structure of the human brain intending to summarize complex information into tangible results. The advantage of these networks is the profound abstraction of relations between input data and the abstracted neuron values with the output data.
While the anthropocentric point of view provides definitions and answers to how humans use technology, the ethological perspective uncovers other aspects by looking closely into "how technology is used." Answering these questions might be valuable to a certain extent, giving more answers to the "how," however, only touching on the "why." #blog #technology
We all know and use technology; the Application of the Sciences to the Useful Arts. Technology is the entirety of strategies, aptitudes, strategies, and forms utilized within the generation of goods or administrations or the achievement of destinations, such as logical examination. Innovation can be the information of procedures, shapes, and such.
Generally speaking, data refers to computer information, which is transmitted or stored. However, there are many other different definitions. These definitions can mostly be tied to types of data. Therefore, data can be viewed as types of information formatted in a specific manner. This leads to the following truth: computer networks can't exist without neither communication protocols, nor data.
Let's look at a process used to discover patterns in large data sets called data mining. The interesting thing about data mining is that it involves different methods at the intersection of statistics, machine learning, and database systems. The overall goal in data mining is to extract the most relevant information from a given dataset and have it structured for further use.
Handling vast amounts of data is a challenge. The recorded measured values and diagnostic data are transmitted from the machines via networks to service centers or directly to the manufacturers. As you can see, such a sophisticated network structure requires techniques and databases from the big data environment.
While describing various algorithms, I have casually used the term "data" without considering what data is. Although I have a vague idea of what it is, honestly, I am not sure. Generally speaking, data refers to computer information, which is transmitted or stored. However, there are many other different definitions. These definitions can mostly be tied to types of data. Therefore, data can be viewed as types of information formatted in a specific manner.
While there has never been more data available for making qualified decisions, it is not guaranteed that these decisions are successful simply because they are based on data. In this blog post, I'd like to examine why wrong choices are made: bias. I have been talking about the issues and weaknesses of AI applications because of bias.