Sunday, February 17, 2019

This is not always the case as was famously demonstrated that the perceptron can not

this is not always the case as was famously demonstrated that the perceptron can not

The perceptron learning algorithm is easy to understand because it makes use of an error signal which is computed as the difference between the true class label and the predicted class label. The error signal then determines by how much the weights should be updated and adjusts this set of weights in such a way that it finds an optimal set of weights that correctly predicts all samples in the dataset. The simplicity of the perceptron learning algorithm also has several drawbacks as we are assuming that there exist a set of weights in the hypothesis space that adequately separates classes. Second, we are also assuming that the dataset is linearly separable into distinct classes. This is not always the case as was famously demonstrated that the perceptron cannot learn the XOR gate because the nature of the data distribution of a XOR gate is such that its samples are not separable by one straight line.

The intuition to take out of this is that to model more complex functions, we need to make use of non-linear activation functions because they are inherently capable of more complex representations. In simple terms, they help us extend our hypothesis space such that we are more likely to come across a set of parameters that solve the learning task as defined by the data.

Thursday, February 14, 2019


The Geometry Engine was implemented as a VLSI integrated circuit in a standard 40-pin package. In order to create such a complex device, Clark and Hannah received advice and assistance from Lynn Conway who led the LSI Systems group at Xerox PARC. Conway was an acknowledged expert in VLSI design and co-author of the standard textbook on the subject, ‘Introduction to VLSI Systems’ along with Carver A Mead of Caltech. Clark and Hannah followed the book’s design methodology closely, using the example given in the book for the design of the Geometry Engine’s arithmetic logic unit. They also received support from Stanford colleague John L Hennessy, an Assistant Professor of Electrical Engineering, making use of Hennessy’s microcode simulation language known as SLIM (Stanford Language for Implementing Microcode) to create the microcode for the device.

The first working prototypes of the Geometry Engine were fabricated and tested at Xerox PARC’s Integrated Circuits Laboratory. Performance was in excess of 100,000 lines per second for a 12-device pipeline, a figure which was comparable with dedicated graphics display systems costing several hundred thousand dollars. The commercial potential of this technology was clear. In April 1981, Clark filed a US patent application for the Geometry Engine. He then set about the task of commercialising the device.

Tuesday, February 12, 2019

Therefore Their Possibility To Receive Rewards And Add Transactions Is Decreasing

Proof of Burn (PoB)

Another alternative system is Proof of Burn, which like PoS, is an energy saving method. Here, the miner sends coins to an unused address and receives points in reward. These points are comparable to the hash-rate of mining and are therefore used to validate blocks and transactions.

It is crucial that this algorithm is only working in connection with other mining possibilities - to be able to burn coins, they have to be created in the first place.

The idea behind it is that an attack would be very difficult and carries an enormous element of risk, unlike the PoS system where we already looked at the “Nothing-at-Stake” issue.

Proof of Importance (PoI)

The system proof of importance, which is used by NEM (one of the largest currencies out there), is not only rare but also has enormous potential.

In this algorithm, the user is collecting points on an importance score. This score can be an indicator how trustworthy the member is. The users are validating transactions, just like in every other blockchain algorithm with the exception that everyone, regardless of his/her wealth, starts at 0.

If a person is making false approvals or tries to betray the system, their rating is lowered. Therefore, their possibility to receive rewards and add transactions is decreasing. You can compare it to financial advisors - you trust the one with good ratings and maybe personal recommendations, but if they are rated negatively you will probably stay away.

Sunday, February 10, 2019

The combining taskbar setting makes the icons smaller and combines icons

The combining taskbar setting makes the icons smaller and combines icons


One final note on the Windows Start menu is that if you right click on Start, you will get a whole other listing of things you can do or open, so give it a try.

The Windows Taskbar is also very customizable, and one of the most noticeable changes you can make is switching from the default view, which is to combine taskbar buttons, as they call them, to never combining them. This is a perfect example of a user preference that can be decided on, and makes no difference in how Windows works, but can enhance your user experience greatly. When you open programs, they take up space on the Taskbar, which allows you to switch back and forth between your open programs. When you open another program, it will add itself to the taskbar, and when you close a program, it will remove itself from the Taskbar.

The combining taskbar setting makes the icons smaller and combines icons. When you have more than one copy of the same program open, it will group them together and show you a preview of the windows side by side (figure 3.6), and you can click on whichever one you want to bring to the front.