In depth mathematical explanation of weight updates in CBOW model of word2vec

00:00 - Introduction and Recap
00:25 - Gradient Descent in CBOW model of Word2Vec for weight Updates
01:54 - Weight Update equation and iteration of CBOW model in Word2vec
03:23 - Error equation and its explanation and simplification of iterations
07:05 - Multiple context word CBOW architecture


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Natural Language Processing Lec 19: Word2Vec - Part 3| Weight Update | Multiple contexts Words

In this video, we will discuss the mathematical underpinnings of weight updates in Word2Vec. We will begin by reviewing the gradient descent algorithm, which is used to update weights in order to minimize the loss function. We will then derive the weight update equations for both the input and hidden layers in Word2Vec. Finally, we will discuss the importance of weight updates in Word2Vec and how they contribute to the accuracy of the model.

Key Points Covered:

Gradient descent is an iterative optimization algorithm that can be used to minimize a loss function.
The weight update equations for Word2Vec are derived using the chain rule.
The input and hidden layer weight update equations are different because the loss function is calculated differently for each layer.
Weight updates are essential for Word2Vec to learn accurate word embeddings.

This video is important for understanding how weight updates work in Word2Vec. Weight updates are a key component of the Word2Vec algorithm, and they are essential for the model to learn accurate word embeddings. By understanding the mathematical underpinnings of weight updates, you will be able to better understand how Word2Vec works and how to improve its performance.

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