We all hate writing unit test cases.

Isn’t it better to invest that time in writing code that actually finishes our job? Sounds about right. Finish up with your tasks hoping that it works and rely on the integration testing to cover all the scenarios. Not always.

When you have a lot of components doing lot of different things, it’s best to consider writing unit tests.

I’ll just quickly mention the advantages that I felt after writing unit tests-
1. You will always know what your component (method, function) is supposed to do.
2. You can always run your tests to check if you have introduced any unnecessary change.
3. If you have a new requirement, write down new tests, or…

Image by Bessi from Pixabay

Here I am awake, again, this late night,
Processing thoughts with all my might.
Want to write with a wee wit,
To turn this tale a tad tenable.
I typed two stories, an allegory,
Another was a fable,
about a cat who crossed the road
but..the plot never thickened
Was it a bad omen?
Or does it have to be a chicken?
The cat spoke — “Hey hooman…
You’re getting yourself into a pickle,
Sitting there with a twisted ankle”.
This new life, this new cage,
I find myself turn into an insect,
It is dreamy, it is transient,
It is twisted, it is Metamorphosis.
They might never call me a smart fool,
Don’t let this go over like a lead balloon.
If you want to hear about the lore,
you can find Kafka on the Shore.

Image by Comfrea from Pixabay

Last week, I was wondering if we could have the power to just take our dataset and treat it like a fuel, feed it into a big fat machine (of course, metaphorically) which needs data as fuel to run and then sit back and relax while all the work is done by that machine. Even though there are some tools available but nothing satisfied me in terms of what I hoped.

Surprisingly, yesterday I stumbled into ludwig, an open source tool by Uber (launched around 3 weeks back) which allows us to have that machine I wished.

All you have…

Neural Arithmetic Logic Unit (NALU)

Hi, you must have heard by now about the new paper published by Google DeepMind — Neural Arithmetic Logic Unit. Last week I skimmed through it and ever since I was curious to understand the nuances of its mathematics. So here I try to write my understanding on the paper after reading it. I don’t think there’s any better way to learn than to tell the world what you know, so here it is.

Today, neural networks as we know it, are able to recognise objects, generate data, translate pretty much anything and can even learn to drive cars provided…

Nilay Shrivastava

Literature | Philosophy | Deep Learning | Android

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