But friends will be friends, as the song goes, and it’s always good to know you can have someone to lean on. So cherish

But friends will be friends, as the song goes, and it’s always good to know you can have someone to lean on. So cherish your friendships, nourish them, ask little, and give generously. One day you’ll look back and see all the lives you’ve touched, and your life will be enriched with meaningful connections.

Another thing that is always on my mind is the fact that, since I moved a lot, I’ve been terrible at keeping in touch with some of my friends. In fact, most of my friendships died because I never took the time to call people or reach out in any way. I’m fully guilty of this; even calling my family is a struggle, but that’s just the way I am. It takes effort to be sociable.

However, once again, just like polynomial transformations, this is computationally expensive and requires a lot of features to be added, Just imagine if you had a training set with m instances and n features gets transformed into a training set with m instances and m features (assuming you drop the original features).

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you do decide to take up this challenge with me, I only ask that you remember this — above all else, be kind to yourself and each other. Compassion and kindness are far more effective than shaming. I know it’s hard in a heated moment, but try to keep as your north star the idea that our goal is to usher in real change, not continue contributing to the polarization of our industry and, by extension, society.

So True. Well, to address the first question, usually you create a landmark at the location of each and every instance in the dataset. This creates many dimensions and thus increases the chances that the transformed training set will be linearly separable.

I’ve done this many times without even realizing it until one of my good friends called me out on it. I’ve known this friend for a long time, and he has a way to tell it like it is, without making me upset. Ever since his observation, I’ve been catching myself in the middle of interrupting a conversation, and even if I still do it, I’m hoping it’s not as bad.

Yes I’m sorry but you do unfortunately need to understand the math in order to code it. If you really hate math, feel free to skip this section, but I highly advise at least trying to understand what is going on to give you a better sense of the problem at hand.

It’s easy to keep myself in a tight bubble with my husband, dog, and close relatives, but maintaining friendships and creating new ones, even if uncomfortable at times, can be very rewarding in the long run.

Luckily for us, the kernel trick does its magic; it makes it possible to compute these higher dimensional relationships without actually transforming or creating new features, and still getting the same result as if you had!

NOTE: In this article, I will be only coding a soft and hard margin SVM. but in the future, I will be writing articles on how to implement the kernel trick in SVM, so be sure to stay tuned for that in the future

Concretely, a large value of gamma will shorten the width of the bell-shaped curve and reduce the range of influence of each instance and resulting in a more irregular decision boundary that wiggles between individual data points. Conversely, a small value of gamma increases the range of influence of each data point, and results in a much smoother decision boundary.

Now, gamma is a special hyperparameter that is a specific to rbf kernels. Referring back to our plot above of the rbf function, gamma controls the width of each bell-shaped function.

Childhood experiences shape us, teenage drama leaves marks, and adult friendships make us even more aware of our shortcomings. We all have that estranged friend we miss, stupid words we wish we could take back, and embarrassing interactions we wish we could erase from our memories.

We’re probably all guilty of not being good listeners at times. When engaged in a conversation, more often than not, we’re thinking about what we want to say instead of truly listening to the other person.

Lastly, when making new friends and finding someone who’s a good listener, I make it all about me. Even if I’m an introvert, I tend to talk too much, overshare, and burden the other person with my problems. Making friends is challenging at any age, and sometimes I feel like I’ve been so out of practice that I don’t even know how to behave anymore.