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In the context of machine learning and neural networks, “batch” refers to a technique used during training where multiple training examples are processed together before updating the weights and biases of the network. Instead of updating the network’s parameters after each individual training example (known as online learning), batches allow for more efficient computation and improved convergence.

Batch training works by accumulating the gradients of the loss function for each training example in the batch and then updating the parameters based on the average gradient. This approach can be advantageous for several reasons. First, it reduces the computational complexity by performing matrix operations on the entire batch, which can be parallelized and optimized. Also it can provide a more stable and smoother optimization process by averaging the gradients over multiple examples, reducing the impact of noise and outliers in individual examples.Batch training exhibits better generalization since the network updates based on information received from diverse examples.

The size of the batch, known as the batch size, is a key parameter that impacts the trade-off between computational efficiency and convergence speed. A larger batch size reduces the frequency of updates but provides a more accurate estimate of the gradients, while a smaller batch size allows for more frequent updates but can increase noise in the gradient estimation. It is common to tune the batch size through experimentation to find an optimal balance for a specific problem and network architecture. Batch training is a fundamental technique in training neural networks, enabling efficient and effective learning from large datasets.

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