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.
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…
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.
If you are beginner (like me) and want to get your foundations gain strength, this article is for you!😀
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…
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.
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.
To build neural networks in TensorFlow with Keras, TensorFlow offers it’s own implementation of Keras…
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.
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. …
“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…
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.
Let me give you few simple questions. Answer them.
If the answer is yes, Choose Recall.
If not ask again,
Now if question to this answer is yes, go with Precision.
Ok let’s dive a little bit by taking examples.
In this example,
Basically we are going to do two things: