People frequently need to negate point in dot when managing forms. Unfortunately, few applications offer the tools you need to accomplish this task. To do something like this usually involves changing between a couple of software packages, which take time and effort. Fortunately, there is a service that suits almost any job: DocHub.
DocHub is a perfectly-developed PDF editor with a full set of valuable features in one place. Modifying, approving, and sharing paperwork is easy with our online solution, which you can use from any online device.
By following these five easy steps, you'll have your adjusted dot quickly. The user-friendly interface makes the process quick and effective - stopping switching between windows. Start using DocHub now!
hello and welcome today we are going to talk about dot products vectors and distances between points so what are vectors and how they are used in machine learning in machine learning there are called feature vectors or parameters so what you see in this slide is a column of City rainfall and temperature so here for each city there are different parameters that are particular to that particular city for example CTA has rain for 100 millimeters or 3.9 three inches it has temperature of 30 degree centigrade or 56 degrees Fahrenheit and those two parameters describe that particular city similarly for city B there are different rainfall and temperatures now in a dataset for such as this one there will be multiple cities and each of the city is described by a specific number for rainfall and specific number for temperature so how would we group these cities together based on these two given parameters and thatamp;#39;s where feature vectors come in as you can see we can create a feature vec
