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DEEPCHECKS GLOSSARY

Backpropagation Algorithm

Backpropagation algorithm explained

It’s a teaching technique used by artificial networks to calculate a gradient descent. To monitor system outputs, desired system outputs are evaluated, and the systems are tweaked by altering weights to narrow the gap as much as possible. The approach gets its characterized by the fact that the masses are reversed.

  • Backpropagation is essentially a technique for fast calculating derivatives.

The difficulty in understanding exactly how adjusting weights and biases impacts the overall performance of an artificial neural network. That is the main reason that held back the widespread use of this algorithm.

Backpropagation is commonly categorized as a form of supervised ML since it requires a known, intended result for every input value in order to compute the loss function gradient. This kind of algorithm is now used in a variety of AI applications.

Benefits of Backpropagation Algorithm

The network is trained in two steps: forward and backward. The net error is determined at the conclusion of the forward pass and should be as little as feasible.

If the present error is significant, the network did not learn from the data correctly. What exactly does this mean? It implies that the present set of values is insufficiently precise to decrease network error and produce correct predictions. As a result, network weights should be updated to decrease network error.

  • The backpropagation algorithm is in charge of changing network weights in order to reduce network error. So its importance is tremendous.

It’s easy to implement a backpropagation algorithm in Python and the following are some of the benefits:

  • Backpropagation is a quick method, particularly for smaller networks. As additional layers and neurons are inserted, the calculation of derivatives becomes slower.
  • The backpropagation method has no parameters to modify, hence there is minimal overhead. The only variables in the method are those associated with the gradient descent technique, such as the learning rate.
  • It is memory-efficient in computing derivatives since it consumes less memory than other optimization methods such as the genetic algorithm. This is a critical characteristic, especially for big networks.
  • This approach is flexible enough to function with a variety of network designs, including CNN, GAN, and fully-connected networks.
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Drawbacks of Backpropagation Algorithm

Despite being the most extensively used technique for training neural networks, the backpropagation algorithm formula has certain drawbacks:

  • The system should be carefully built to avoid disappearing and bursting gradients, which impact how the network learns. For instance, the gradients produced by the sigmoid function might be very low, close to zero, preventing the network from updating its weights. As a consequence, no learning takes place.
  • For each backward pass, the backpropagation method evaluates all cells in the system identically and computes their derivatives. Even though dropout layers are utilized, the dependents of the neurons that have been dropped are computed and then dropped.
  • Backpropagation assigns credit via infinitesimal effects (partial derivatives). As one analyzes larger and more non-linear functions, this might become a severe concern.
  • Layer i+1 a forward pass must wait for layer i’s computations to complete. Layer I must wait for layer i+1 to execute in the backward pass. This locks all network layers, preventing them from being updated until the rest of the network executes forwards and propagates errors backward.
  • It anticipates that the error function will be convex. Backpropagation may become trapped in a local solution for a non-convex function.
  • For the algorithm to work, both functions- error, and activation must be differentiable. It is incompatible with non-differentiable functions.