Coding Snap

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Starting new hashtags for our page. This is the first one where we'll try to precisely define the most used Machine Learning terminologies.

Hope this will help✌️
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[Zoom-In to read] ** OFF TOPIC**
Sharing the information about **ar

This post shares what the problems look like, the languages you can code in and other details too.
Further, I'll keep you updated.

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**ar @ Bangalore, India

🤔You may wonder what is Kernalisation or kernels?
So according to Wikipedia, the definition is :- Kernelization is a technique for designing efficient algorithms that achieve their efficiency by a preprocessing stage in which inputs to the algorithm are replaced by a smaller input, called a "kernel".
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SVM (Part-5) 👉With this post, we have covered almost all the major concepts related to SVM. The algorithm formulation; the optimisation; and the kernels.

👨🏻‍💻Check the recent posts for SVM previous posts.

Hope this adds some value to your Instagram feed🙂.

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Source of my Learning: Hands on machine learning with scikit-learn and Tensorflow By Aurelien Geron.
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@ Quarantine 2020

SVM(Part-4) 👉 Coding the SVM down in Python. The algorithm used in this Post is Pegasos (discussed in earlier post)

The basic idea of pegasos is to randomly initialise weight and bias and proceed by using batch gradient.

Hope that helps.🧐

If not leave your query in the comment section 🤸

If you like the content please consider following the page and sharing in your circle ❤️

Just follow the page and keep brushing up your concepts. ;)

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@ Quarantine 2020

👉Interior Point (IP) methods: IP methods
cast the SVM learning task as a quadratic optimization problem subject to linear constraints.
The constraints are replaced with a barrier function. The result is a sequence of unconstrained problems which can be optimized very efficiently using Newton or Quasi-Newton
methods.
👉Decomposition methods: To overcome the quadratic memory requirement of IP methods,
decomposition methods such as SMO and SVM-Light tackle the dual representation of the SVM optimization problem, and employ an active set of constraints thus workingon a subset of dual variables. In the extreme case, called row-action methods, the activeset consists of a single constraint.
👉Primal optimization: Tackling the
primal objective directly was studied, for example, by Chapelle , who considered us￾ing smooth loss functions instead of the hinge loss, in which case the optimization problem
becomes a smooth unconstrained optimization problem. Chapelle then suggested using various optimization approaches such as conjugate gradient descent and Newton’s method. We
take a similar approach here, however we cope with the non-differentiability of the hinge￾loss directly by using sub-gradients instead of gradients.

Now in next post we'll look into codes and kernels.

If you like the content please consider following the page and sharing in your circle ❤️

Just follow the page and keep brushing up your concepts. ;)

For pegasos, you can read this research paper :
https://www.google.com/url?sa=t&source=web&rct=j&url=https://ttic.uchicago.edu/~nati/Publications/PegasosMPB.pdf&ved=2ahUKEwjny5q586XpAhUr7HMBHTygCO8QFjACegQIAhAB&usg=AOvVaw0NEf9uKZVjxKtrCE7y5KfR

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@ Mumbai - City of Dreams

Here is the first post on Support Vector Machines where I have tried to quote the concepts in an easy manner along with the formulations and diagrams.

This post talks about the main objective of Support Vector Machines, then the real quick concept of Hyperplanes and support vectors along with maximal-margin Hyperplane. Followed by understanding the approach and formulating the objective and optimising.

After all this, we'll find that the function achieved does not serve the purpose well as it tries to classify all the data points correctly and eventually overfits. This is termed as Hard-margin SVM where the motives of the model is to classify the points correctly rather than to achieve a generalised version of the model.

So, there comes Soft-margin SVM that handles the outliers and gives the more generalised model. We'll see that in the next post.

(For better understanding you can watch videos tutorials, if you are finding it hard to understand. It is really difficult to portray everything in an instagram post).

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@ Mumbai - City of Dreams

The content on the page from now on is going to change a bit, we'll be exploring through codes and work on implementation more than on the theory part because that's what we have been doing so far.

Hopefully, it will help us in revising and keeping the concepts clear.

If you are an aspiring Android developer, Kotlin programming language should be on your list of things to learn. Kotlin is a simple, but powerful language that makes Android development more expressive and helps you write higher quality apps. You can use the language easily with your choice of IDEs be it Android Studio or IntelliJ.

Google is introducing “30 Days of Kotlin with Google Developers” to improve your understanding of Kotlin and apply it in real projects. Google has created specific courses for Android developers at different expertise levels.

You will have 30 days (7th May to 7th June) to learn and build an app using Kotlin, and in the end, submit your work. Sounds fun?

Register (link in bio) by 7th May 2020, 5 PM IST.

This is open to developers based in India only

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One of the most versatile algorithm used in and .

Cheatsheet for Clustering.

The selected candidate has to write articles, design content, and help in framing strategy to their co-workers for the better optimization of the website.