What is the difference between SGD and GD?

In Gradient Descent (GD), we perform the forward pass using ALL the train data before starting the backpropagation pass to adjust the weights. This is called (one epoch). In Stochastic Gradient Descent (SGD), we perform the forward pass using a SUBSET of the train set followed by backpropagation to adjust the weights.

What is the difference between stochastic and mini-batch gradient descent?

In the case of Stochastic Gradient Descent, we update the parameters after every single observation and we know that every time the weights are updated it is known as an iteration. In the case of Mini-batch Gradient Descent, we take a subset of data and update the parameters based on every subset.

What is the difference between gradient descent and gradient ascent?

The gradient is the vector containing all partial derivatives of a function in a point. Gradient descent finds the nearest minimum of a function, gradient ascent the nearest maximum. We can use either form of optimization for the same problem if we can flip the objective function.

What is correct about stochastic gradient descent?

Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable).

What’s the difference between Incline and gradient?

As nouns the difference between incline and gradient is that incline is a slope while gradient is a slope or incline.

What is the difference between gradient descent and backpropagation?

Back-propagation is the process of calculating the derivatives and gradient descent is the process of descending through the gradient, i.e. adjusting the parameters of the model to go down through the loss function.

What does stochastic mean in Stochastic Gradient Descent?

The word ‘stochastic’ means a system or a process that is linked with a random probability. Hence, in Stochastic Gradient Descent, a few samples are selected randomly instead of the whole data set for each iteration.