Visarga ( ः), ह and ह् are indeed disparate, and so are anusvara ( ं - ṃ), न, न्, म and म्.

Learn about **LSTMs**, and see why they work the way they do by **interacting** with one!

It's astonishing to see that by using a very **simple mechanism**, we can somewhat generate the pattern long and short term memory are supposed to follow.

LSTM or Long-Short Term Memory - Learn by Interacting

Let's make the computer play thousands of times betting on different strategies to see if it's really a fallacy. And more!

MNIST: The 'Hello World' of Machine Learning Programming

Let's get to the basic physics of flying using a thrust that is less than the weight of the body. **It's not only possible, but it's so natural** that we should actually ask why do we need any thrust at all!

With a Thrust-to-Weight Ratio of Less Than 1!

What is a Tensor - in real physical sense? Is it a complex **physical entity**, a **double vector**, or just a **mathematical notation with no physical meaning**? Have an understanding from different points of view.

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Create private equivalent of a fork of a public repo on github

In this chapter, we'll be looking at the description of **recurrent neural network (RNN)**.

Recurrent Neural Network - RNN

Save any image from clipboard in general

Setup audio both with and without an external dock. Force detection of audio devices including speakers and microphones.

VS Code in itself is developer's delight. Add remote development to it through WSL, Docker and Dev Containers - and it is heaven! Well, for a coder at least.

Code, develop, build, test and showcase like a Pro

धन्यवाद बंगाल! तुम्हारा बलिदान स्वर्णाक्षरों में लिखा जाएगा।

তোমাকে ধন্যবাদ

See the Information Theory in a new light. Understand intuitively how the **information of an event naturally relates to its probability and encoding**

And, it's nice to know that understanding information theory helps in getting some of the aspects of **machine learning** as well.

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After constructing the theoretical framework in the last chapter, **we will now be dealing with some of the practical difficulties**.*From traditional regression to neural networks - it's not that big a leap as you might think.* In this book, let's get a peek into this transition while appreciating how animal kingdom is already using this strategy. We will be taking help from our friend - *intuition* - time and again.

Let's Look at some of the Practical Difficulties

In this chapter, we will be sharpening our **theoretical tools** and sneak our way into the **mathematics of neural networks.***From traditional regression to neural networks - it's not that big a leap as you might think.* In this book, let's get a peek into this transition while appreciating how animal kingdom is already using this strategy. We will be taking help from our friend - *intuition* - time and again.

Chapter 2: Beating the Theoretical Difficulties and Making Gradient Descent Work

In this chapter, we will be looking at the basics - the **idea of prediction**, using **traditional regression** and moving towards **learning based methods**.*From traditional regression to neural networks - it's not that big a leap as you might think.* In this book, let's get a peek into this transition while appreciating how animal kingdom is already using this strategy. We will be taking help from our friend - *intuition* - time and again.

Chapter 1: The Idea - Why Machine Leaning? What makes it different from linear and other simple regressions?

Let's derive the probability equations that govern the predictions of the famous **Monty Hall problem**. Doing it for generalized number of total, closed and open doors gives us a better understanding and deeper satisfaction!

A very beautiful, counterintuitive and yet so simple puzzle! Have a look here, and see for yourself intuitively why it works.

This online tool can be used to download (extract) captions / subtitles from YouTube in the language of your choice and formats such as plain text, vtt or srt.

थोड़ा ठहरें, सोचें, और इस 'धार्मिक भावनाओं की ठेस' वाले नर्क से बाहर निकलें। संसार सुंदर है, और आप और भी ज़्यादा। थोड़ा ऊपर उठें और दूर तक देखें।

ध्यान दें, कहीं फट न जाए!

कई बार भावावेश में हम अपने अहंकार और अपनी मूर्खता को अनदेखा कर जाते हैं। थोड़ा सा विचार कर के हम इस से बच सकते हैं।

इसे कपट कहें या पाखण्ड

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Let's dissect one of the (if not **the**) most beautiful equations in mathematics.

सत्य और परोपकार का रास्ता ही वेदान्त का रास्ता है, और इस रास्ते पर चलने वाला हर व्यक्ति वेदान्ती है, फिर चाहे वो इस कहानी के नायक भगत जैसा निपट गँवार ही क्यों न हो!

साभार - kahaani.org

"क्या इस तूफानी रात में आपकी झोपड़ी में एक अनजान मेहमान के लिए जगह है?" "घर की बात क्या करनी, दिल में है, फिर घर में तो बन ही जाएगी!"

Why do we use standard deviation at most places when we have conceptually easier to understand mean absolute division? Let's try to figure it out.

A Simple yet Interesting Question in Statistics

The story of a kid whose courage and morals were invincible.

एक बालक की प्रेरणात्मक कहानी जिसका सत्य के प्रति प्रेम अनुपम व साहस अजेय थे।

Shackles, be those of iron or gold, serve just one purpose - tying you up, stopping you. And stopped, you are done!

ज़ंजीर चाहे लोहे की हो सोने की, दोनों का काम एक ही है - बांध लेना, रोक लेना। और जो रुक गए तो गए काम से!

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Let's face it - there is so much pain in the world. Along with pleasure of course, but I've no problem with the latter! Why, for God's sake, why?

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