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Computer Science Student | Aspiring Machine Learning Engineer |

SQL, NoSQL, what next?

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We all are familiar with Relational and Document data model and databases which rely upon these model to give a logical structure to our data.

But there are few more data model that is worth pondering upon.

Graph-Like Data Models are not so popular among mass but power many data systems like Recommendation systems and is the need of the hour for emerging fields like Data Science, Analytics and Artificial Intelligence for scalable solutions.

The need…

Anyone who has worked with Relational databases knows that the data is organized in what is called as relations, where each relation is an unordered collection…

What’s the best way to start reading research papers? Don’t know? This will definitely help…

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Being better at Deep Learning isn’t a feat achievable in few days or weeks. It might take months or years as the field keeps evolving at a rapid pace.

The solution is simple…

Read research papers and keep up with the new trends and emerging advances.

But where to start?

If you are beginner (like me) and want to get your foundations gain strength, this article is for you!😀

First off, Begin Compilation

Compile a list of papers that interests you from various sources.

Try taking a shot at one paper, skim it; don’t like it, skip. Try next one, skim it; like it, read a related paper…

A simple introduction to creating various Neural Networks in TensorFlow.

What is Keras?

Keras is a high-level Deep Learning API(Application Programming Interface) that allows us to easily build, train, evaluate, and execute all sorts of neural networks. What is does is abstract away the implementation of various Deep Learning libraries like TensorFlow, Microsoft Cognitive Toolkit(CNTK), and Theano.

What is TensorFlow?

It’s a Deep Learning Library and along with that is provides a large set of tools for numerical computation, and large-scale Machine Learning. It also provide TensorBoard for visualization of model, TensorFlow Extended (TFX) to productionize TensorFlow projects, and much more.

Let’s get started

To build neural networks in TensorFlow with Keras, TensorFlow offers it’s own implementation of Keras…

Some techniques really makes a difference.

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Life’s way of teaching is very different from what we are used to in schools.

Schools teachers you, take tests, 2–3 times that’s it. You pass, good. You fail, oh poor you.

Life is little different. It test you, you learn. You learn not to make those mistake again. It again take test and keep taking until you don’t learn it the best way.

This testing/learning loop never stops in during Life time.

The Story

I remember in my first year of my engineering(2020), I wanted to dive in ML.

The mistake I did was to learn it the school’s way. …

Books are a great resource to learn Data Science. These are the books I pledged to read in 2021 to start off my exploration.

“I do believe something very magical can happen when you read a book.” — J.K. Rowling

When I wanted to explore the machine learning world, I knew I would be requiring good books. Books that would teach me the ins and outs of this world.

I wandered around on the internet, as a curious explorer. I found a few handfuls of books that were not only great for beginners but also provided a hands-on approach to entering this world.

So what are these books for?

These are the few broad topics which we are looking to cover by reading these…

Outliers are one of the most peculiar things a Data Scientist don’t want to see in their datasets. Statistics has an answer.

Outliers in a dataset as seen in a scatter plot.

So First Off, What is an Outlier?

An outlier is any piece of data that is at abnormal distance from other points in the dataset. To us humans looking at few values at guessing outliers is easy.

Take a look at this, Can you guess which are outliers?

[25, 26, 38, 34, 3, 33, 23, 85, 70, 28, 27]

Well my friend, here, 3, 70, 85 are outliers.

But consider this, as a Data Scientist, we might have to analyze hundreds of columns containing thousands or even millions of values. And you will immediately come to the conclusion that this method of guessing is just not feasible.

A Practical approach to selecting evaluation metric for you model, described in simple words.

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Let me give you few simple questions. Answer them.

Are the consequences of detecting False Negatives are Highly severe?

If the answer is yes, Choose Recall.

If not ask again,

Are the consequences of detecting False Positives are Highly severe?

Now if question to this answer is yes, go with Precision.

Ok let’s dive a little bit by taking examples.

Predicting whether or not a patient has a tumor.

In this example,

  • Detecting False Negatives is said to occur when the patient has Tumor BUT the model didn’t detected it. Now this case has very high Consequences. Since we didn’t detected the tumor, doctor will not the carry out necessary treatment. Fatal.
  • But False Positives are not really our concern. Why? Say, the model detected a tumor, which in…

Update webpage elements without refreshing

Maybe you want to make changes to database and also reflect that back on the webpage for a user on a click of a button; without reloading, sort of like an app, right? Well what you are looking for is AJAX (Asynchronous JavaScript And XML).

Basically we are going to do two things:

  1. Make a call to an API (Application Programming Interface) written in Django along with your project (Yes, you are going to make an API now! Yayyyy!). The API is going to perform some operation on database.
  2. Then our API, sends back data in JSON(JavaScript Object Notation) format…

Anurag Dhadse

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