What is mini-batch?
A small subset of data used for training AI models
mini-batch explained in plain English
Analogy
Think of a mini-batch like a small class of students. Just as a teacher might divide a large group of students into smaller classes to make learning more manageable, a mini-batch divides a large dataset into smaller groups to make training an AI model more efficient.
Example
Imagine training a self-driving car to recognize stop signs. A mini-batch might include 32 images of stop signs from different angles and lighting conditions. The model would process these 32 images, learn from them, and then move on to the next mini-batch.
How is mini-batch used?
Mini-batches are used in stochastic gradient descent, a popular algorithm for training neural networks. The model processes one mini-batch at a time, making predictions and adjusting its parameters based on the results.
Common misconceptions about mini-batch
History
The concept of mini-batches has been around since the early days of neural networks. However, it gained popularity with the development of stochastic gradient descent and the rise of deep learning.
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