Best Regularization Quotes

"Adversarial training and virtual adversarial training also make it possible to regularize your model and even learn from unlabeled data."

"The RNN model, even with the regularization here, does possibly tend to overfit more but because it's able to capture more complex dynamics, its bias overall might be lower and that translates to an overall gain of $4 on the $1,000 that we invested."

"Regularization introduces a penalty by shrinking the coefficients towards zero, reducing the overall model variance."

"L2 regularization assigns a penalty by shrinking the coefficients towards zero, but they will never become exactly zero."

"L1 regularization is a shrinkage technique that aims to solve overfitting by shrinking some of the modal coefficients towards zero and setting some to exactly zero."

"What is a good fit? A linear curve that best fits the data is neither overfitting or underfitting but is just right."

"Regularization techniques are used to calibrate the linear regression models and to minimize the adjusted loss function and prevent overfitting or underfitting."

"Ridge regression modifies the overfitted or underfitted models by adding the penalty equivalent to the sum of the squares of the magnitude of the coefficients."

"Regularization is taking the guesswork out."

"Bayesian inference is data plus regularization, which means we can fit a model to data when we have a big model and we fit to Big Data. Our estimates can be very noisy; regularization is a term, a general term for taking noisy estimates and making them more stable."

"Now the methods we have been discussing go by the name regularization methods. This is because the original problem is imposed as we discussed, but by adding the additional term in the minimization, you regularize the problem."

"Multitask learning is a form of regularization because you're giving it these auxiliary loss functions that should help learn representations."

"XGBoost is one of the implementations of the gradient boosting concept but what makes it unique is that it uses a more regularized model formulation to control overfitting."

"Regularization is really important when you're building machine learning systems."

"Dropout regularization is one of the techniques that's used to tackle overfitting problem in deep learning field."

"SVM seems to work pretty well for in most cases but it's particularly good when we have little training sample because it's a regularized method."

"Regularization is providing a motivation to use multiple features and not consider one too heavily."

"The regularizer penalizes really complicated solutions."

"The regularization hurts your training performance but it may increase your test performance."

"XGBoost uses a more regularized model formalization to control overfitting, which gives it better performance."

"L1 regularization is actually very well suited to these types of risk stratification problems."

"We can see the impact of regularization on the mean squared error."

"We get an intuitive regularization parameter which is called smoothing, and the ability to model nonlinearities automatically."

"Batch normalization actually reduces the need for doing regularization."

"Regularization means finding a set of estimates that are neither overfit nor underfit; they make good out-of-sample predictions."

"Priors can do a lot to regularize estimates and tighter priors often give us better out of sample predictions."

"You can just make a relatively small k and then prune back to do the regularization."

"So now let's define our baseline model and see how regularization will improve it."

"You can think of [tangent prop] as a regularizer that says... the overall derivative of the function in the direction of the spanning vectors of that plane should be zero."

"That's because you'll get better regularization, you'll get better estimates, less overfitting, and you'll avoid underfitting as well."

"Weight Decay helps to avoid overfitting because it reduces the number of parameters that you have."

"This is called Tikhonov regularization, and it has lots of applications."

"Adding this term is effectively equivalent to regularizing our model."

"To fight variance, we do things like increase regularization or collect more data."

"Regularization is particularly important because if you come up with good regularizes and did this requires thought then you can actually you have a mechanism for controlling complexity."

"Dropout is a really effective form of regularization, widely used in neural networks."

"Regularization means anything you do to the process of fitting your data that makes your prediction function fit the training data less well, in the hopes that it's going to fit new data better."

"The magnitude of lambda balances the trade-off between the complexity of the function and how well we fit the data."

"The BART model is a sum of trees model with regularization priors."

"Regularization is one of the key ideas that you should keep in mind as you do anything in machine learning."

"Overfitting on the training data isn't a problem, but nevertheless you need regularization to make sure that your models generalize well to independent test data."

"Dropout has just emerged as in general the best way to do regularization for neural nets."

"Support vector machine is linear predictions, linear hypothesis space, it's regularized empirical risk minimization with the hinge loss and L2 regularization penalty."

"Regularization controls the value of parameters, giving not so important variables very low weight or sometimes zero weight."

"Regularization is a technique that will raise the estimates or it shrinks the coefficients to zero."

"Multitask learning can be viewed as a form of regularization."

"Multitask learning in many ways can be viewed as a form of regularization because it essentially gives you more data."

"The secret, in my opinion, to modern machine learning techniques is to massively overparameterize the solution to your problem and then use regularization."

"Tikhonov regularization... it's adding alpha to the least squares problem."

"Dropout is what I explained about dropping some of the actions in order to not overfit."