Gradient Descent and Back-Propagation. The gradient of the loss function with respect to each weight in the network is computed using the chain rule of calculus. This gradient represents the steepest slope of the loss function at each node. The gradient is calculated by propagating the error bac...
Shor NZ, Gamburd PR (1971) Certain questions of convergence of generalized gradient descent. Kibernetika 8 (no 6): 82–84; Cybernetics 8:1033–1036Shor, NZ, Gamburd, PR (1971) Certain questions of convergence of generalized gradient descent. Kibernetika 8: pp. 82-84...
You can use many optimizers based on various factors, such as the learning rate,performancemetric, dropout, gradient, and more. Following are some of the popular optimizers: AdaDelta AdaGrad Adam Momentum RMSprop Stochastic Gradient Descent 47. What is the use of ArrayFlow and FeedDictFlow in Te...
37. What is exploding gradient descent in Deep Learning? Exploding gradients are an issue causing a scenario that clumps up the gradients. This creates a large number of updates of the weights in the model when training. The working of gradient descent is based on the condition that the updat...
No, gradient descent methods do not always converge to the same point because they converge to a local minimum or a local optima point in some cases. It depends a lot on the data one is dealing with and the initial values of the learning parameter. 33. What is the difference between Sup...
27. What is the difference between batch gradient descent and stochastic gradient descent?Batch processes the full dataset in each step, whereas stochastic processes one sample at a time, which can be faster but noisier.28. What are Generative Adversarial Networks (GANs)?
On the other hand if our model has large number of parameters then it’s going to have high variance and low bias. So we need to find the right/good balance without overfitting and underfitting the data. [src] 2) What is gradient descent? [src] [Answer] Gradient descent is an ...
Gradient descent is an optimization algorithm used to minimize the loss function in machine learning by iteratively adjusting model parameters in the direction of steepest descent. 14. What is deep learning? Deep learning is a subfield of machine learning that focuses on neural networks with multiple...
Ensemble methods work by building models sequentially, where each new model corrects errors made by the previous ones, commonly leading to a strong predictive performance, especially on complex datasets. Optimization Techniques: Understanding gradient descent and its variants like SGD, Mini-Batch ...
Using gradient descent to estimate the parameters of a multiple linear regression modelFun finding duplicate elements in an array Posted on September 2, 2016 I recently saw a fun programming challenge of trying to find a duplicate element in an array. The problem was stated as follows. You ha...