Gradient descent is an optimization algorithm used in training neural networks. It aims to minimize the loss function by iteratively adjusting the network's weights in the direction of steepest descent. Besides Gradient Descent, there are other optimization algorithms such as Stochastic Gradient Descent (SGD), Momentum, Adagrad, RMSprop, and Adam, each with its own benefits and suited to different types of problems and data.
Gradient descent and backpropagation are two key processes in neural network training. Backpropagation is an algorithm used to compute the gradient of the loss function with respect to each weight in the network, effectively indicating the contribution of that weight to the total error. Gradient descent, on the other hand, is an optimization algorithm that uses the gradients computed by backpropagation to update the weights in a manner that minimizes the loss function.
The process of updating weights involves several steps: the forward pass where the input data is passed through the network, the computation of loss which compares the network's prediction to the true label, the backward propagation (backpropagation) which computes the gradients of the loss with respect to the weights and biases, and finally, the update of weights and biases using the gradient descent algorithm based on the computed gradients.
In machine learning, it is essential to preprocess the data before training a model. One common preprocessing technique is feature scaling, which ensures that all features are on a similar scale. When applying feature scaling, such as with the StandardScaler, we need to consider how to transform both the training and test data. The fit_transform() method is applied to the training data. This step calculates the necessary scaling parameters, such as the mean and standard deviation, based on the training data. Then, it applies the calculated parameters to standardize the training data. By using fit_transform(), we ensure that the training data is scaled appropriately based on its own distribution. However, when it comes to the test data, we cannot calculate new scaling parameters because it would introduce information leakage from the test set. Instead, we want to apply the same scaling transformation as learned from the training data. To achieve this, we use the transform() method on the test data. It applies the same scaling parameters obtained from the training data to transform the test data. By following this approach, we maintain consistency in the scaling process between the training and test data. This is crucial for accurate model evaluation and prediction because the model expects input data on the same scale that it was trained on. Including this explanation in your blog post will provide readers with a clear understanding of why fit_transform() is used on the training data and transform() is used on the test data when scaling features in machine learning.