Explain the concept of Backpropagation Algorithm

What is Backpropagation Algorithm?

Backpropagation Algorithm is a machine learning technique that is used to train artificial neural networks. It is a gradient-based optimisation approach that computes the gradient of the loss function in relation to the parameters of the model. The gradient is then used to update the parameters of the model in the direction of the sharpest decrease.

The learning rate is a hyperparameter that regulates the size of the updates to the model parameters. A large learning rate will cause the model to learn quicker but may increase the chance of overfitting the training data. A smaller learning rate will slow the model to learn and may also cause the model to take longer coverage.

Why we Need Backpropagation Algorithm?

We need backpropagtion algorithm because of the following reasons:

  • Backpropagation Algorithm is fast, simple and easy to program.
  • Backpropogation Algorithm has no parameters to tune apart from number of inputs.
  • Flexible method as it does not require prior knowledge about the network.
  • Backpropogation Algorithm does not need an special mention of the features of the function to be learned.

Explain the role of learning rate: What effect does it have when the learning rate is increased? What impact does a decreased learning rate have?

When the learning rate is increased, the following effects can occur:

Faster Convergence: A greater learning rate allows the model to aggressively change its parameters, resulting in faster convergence during training. This is useful when working with huge datasets or complex models since it accelerates the learning process.

Overshooting: A high learning rate may cause the model to exceed the ideal result. It might cause oscillations or instability in the training process, causing the loss to grow rather than decrease. This can impair the model’s capacity to find an optimal solution.

Risk of Missing the Global Optimum: As the learning rate increases, the model may quickly converge to a suboptimal solution or become stuck in a local minimum. It may not properly explore the parameter space in order to identify the global optimum.

When the learning rate is decreased, however, the following impacts can be observed:

Slower Convergence: A lower learning rate delays the learning process since the model updates its parameters more slowly. More iterations or epochs are required to achieve convergence. This can be useful in situations requiring fine-tuning or accurate parameter modifications.

Reduced Overshooting: A slower learning rate minimises the risk of exceeding the ideal answer and aids in the stabilisation of the training process. It enables the model to produce fewer, more controlled updates, preventing oscillations and resulting in smoother convergence.

Enhanced Generalisation: Lowering the learning rate can assist the model in generalising to previously unknown data. It enables the model to explore the parameter space , perhaps discovering a better global optimum and avoiding overfitting.

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