Converting words to numbers, Word Embeddings | Deep Learning Tutorial 39 (Tensorflow & Python)

Machine learning models don't understand words. They should be converted to numbers before they are fed to RNN or any other machine learning model. In this tutorial, we will look into various techniques for converting words to numbers. These techniques are,
1) Using unique numbers
2) One hot encoding
3) Word embeddings

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#wordembeddings #wordembeddingsexplained #deeplearningtutorial #convertingwordstonumbers #wordembedding #wordembeddingnlp #tensorflowwordembedding #wordembeddingwithnlp #nlpforwordembedding #wordembeddingkeras #whatiswordembedding #wordembeddingnlptutorial

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❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.

[sunset]

This is so far the best video i saw on understanding word embedding concept.

[WahranRai]

The good example for converting word to numbers is the coding of color in RGB :
black = (0, 0, 0) red =(255, 0, 0) yellow=(255, 255, 0) green = (0, 128, 0) blue = (0, 0, 255) etc....

[funyn]

this video has been much more helpful than any other videos I've found thank you

[raulpetcu]

you are my savior, my friend. God bless you <3

[sumit]

loves your videos...thanks sir...you are the

[linghaoyi]

Thank you for your excellent tutorials. Merry Christmas and Happy New Year!

[kirandeepmarala]

Really Dhaval Sir, No One Can Explain Better than This.Your way of Teaching is Unique and Intresting. Thank you So Much.

[kmnm]

Hi Dhaval,

The analogies with which you start every DL concept in your videos are pure gems. Here the examples of players and Australia - with the float value for each dimension ( feature ) is pure streak of genius. One just goes spellbound when the whole concept become so clear. It is said that great teachers are the ones who make complex things look simple. And when one explains in simple terns it means that the person has understood it completely. It fits you perfectly. Before watching this video, though I could understand what word vectors were in terms of dimensions but the real meaning(conceptual understanding) become immensely clear when you presented them with values like - Person, Fitness, Location, Has Government - was mind blowing.
Just one question - let us say that we decided the dimensions to be 100 for each word in an embedding scenario. Should all the values of the dimensions add up to 1 ( like probabilities adding up to one)? Just want to get that clarified. Thanks again for excellent session, Krish

[krishnarao]

Awesome explanation about word embeddings.

[mindmumi]

Very clear explanation for beginners 👍

[theophilus]

Thank you so much for the good work you are doing here...

[EM-wtqe]

Very informative, makes perfect sense

[shreyasb.s]

Nice video.
"I am working in Apple and I will eat Apple later"
Apple giving different meaning in two sentences.
How to handle this in word embedding ?
This is very famous tricky interview question.
Could you please try to make one separate video on this.

[PritishMishra]

Sir, please make End to End NLP tutorials!

[maheswarareddygajjala]

you can put height, weight, runs tked etc for explanation

[meysamjavadzadeh]

love that avengers painting on the wall😂

[LokeshKumar-pmzu]

Approximately how many more tutorial videos are there gonna be in this entire DL series?

Bdw, the tutorials are too intuitive, keep it up 👍👍

[abypaul]

Good explanation. Some things not clear like if we are training the model with data from wikipedia data, how are we coming up with the feature data for each words. Like as you explained for the words - Dhoni, cummins and Australlia these were the feature data (person, location, fit etc), how to comeup with feature data for each words?
.

[omolarasilverstone]

Pls sir, will this ur tutorial be good for data science or is different

[amruth]

how do we define the features for every word in the language, like in this example in the video it works well for a country or a person or a cricketer what is a word red is used here here is no feature called color, if we increase the features then there will more number of features again if we want to compute for every word in the language by describing the features