Main Types of Gradient Descent | Batch, Stochastic and Mini-Batch Explained! | Which One to Choose?
๐ฅ There are three main types of gradient descent: Batch, Stochastic and Mini-Batch. Batch gradient descent takes all observations for gradient computation, which is both accurate and resource heavy. Stochastic takes only one random observation from the data which is a poor approximation but introduces randomness. Mini-Batch is the mix of two, takes a random sample from the data.
Each type has its own advantages and disadvantages. Batch gradient descent requires more resources and converges confidently to a minima (sometimes to a local minima), while stochastic converges faster due to frequeโฆ
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Chapters (8)
Introduction.
0:10
Batch gradient descent.
0:20
Stochastic gradient descent.
0:34
Mini-batch gradient descent.
0:48
Batch gradient descent pros and cons.
1:21
Stochastic gradient descent pros and cons.
1:51
Mini-batch gradient descent pros and cons.
2:10
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