Word Embeddings from Scratch | Word2Vec

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How do we represent word in a way that ML models can understand it. Word Embeddings is the Answer. There are many different ways of learning word embeddings such CBOW, Skipgram, Glove, etc.
In this video we, will build word2vec from scratch and train it on a small corpus.

Chapters:
0:00 Introduction
2:03 Distributional Hypothesis
3:51 Training data for Word2Vec
7:04 Model Architecture
10:59 Training word2vec
14:36 Visualising the Embeddings
17:22 Tips and Tricks for Word2Vec

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Atif Adib

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Комментарии

thank you so much for your videos, they are so helpful !

yousrayouyou

This is top class. You really nailed the topic here. 🔥

mountainking

I went to school with this guy. Next big thing in Machine Learning

GhtsGameplay

awesome, may i suggest increasing the volume of the video before upload? It happens to be a bit soft but thanks so much for the tutorial.

paulsoh

didn't need to pass the input to a one hot vector? and the out too?

ASdASd-krft

Why we are averaging along column wise instead of row wise (word wise) since each row has embeddings of each unique words why not we are i understand that computing along row wise results in large dimensions but how column wise makes meaningful?

Please clear my doubt
Thank you in advance

abubacker

Why don't you validate the embedding by validation set on your training?

ambrosentk

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