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Accepted

Choosing a learning rate

Is the learning rate related to the shape of the error gradient, as it dictates the rate of descent? In plain SGD, the answer is no. A global learning rate is used which is indifferent to the error ...
• 4,159
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Does gradient descent always converge to an optimum?

Gradient Descent is an algorithm which is designed to find the optimal points, but these optimal points are not necessarily global. And yes if it happens that it diverges from a local location it may ...
• 13.3k

Should a model be re-trained if new observations are available?

When new observations are available, there are three ways to retrain your model: Online: each time a new observation is available, you use this single data point to further train your model (e.g. ...
• 481
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Why not always use the ADAM optimization technique?

Here’s a blog post reviewing an article claiming SGD is a better generalized adapter than ADAM. There is often a value to using more than one method (an ensemble), because every method has a weakness.
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Are there any rules for choosing the size of a mini-batch?

In On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima there are a couple of intersting statements: It has been observed in practice that when using a larger batch ...
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Should a model be re-trained if new observations are available?

Once a model is trained and you get new data which can be used for training, you can load the previous model and train onto it. For example, you can save your model as a ...
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• 396

Choosing a learning rate

Below is a very good note (page 12) on learning rate in Neural Nets (Back Propagation) by Andrew Ng. You will find details relating to learning rate. http://web.stanford.edu/class/cs294a/...
• 361
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Difference between RMSProp with momentum and Adam Optimizers

(My answer is based mostly on Adam: A Method for Stochastic Optimization (the original Adam paper) and on the implementation of rmsprop with momentum in Tensorflow (which is operator() of struct ...
• 614
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How many features to sample using Random Forests

I think in the original paper they suggest using $\log_2(N +1$), but either way the idea is the following: The number of randomly selected features can influence the generalization error in two ways: ...
• 5,959

local minima vs saddle points in deep learning

Let me give an explanation based on multivariate calculus. If you have taken a multivariate course, you will have heard that, given a critical point (point where the gradient is zero), the condition ...
• 5,614

Does gradient descent always converge to an optimum?

Asides from the points you mentioned (convergence to non-global minimums, and large step sizes possibly leading to non-convergent algorithms), "inflection ranges" might be a problem too. Consider the ...
• 1,147

Guidelines for selecting an optimizer for training neural networks

AdaGrad penalizes the learning rate too harshly for parameters which are frequently updated and gives more learning rate to sparse parameters, parameters that are not updated as frequently. In several ...

Choosing a learning rate

Selecting a learning rate is an example of a "meta-problem" known as hyperparameter optimization. The best learning rate depends on the problem at hand, as well as on the architecture of the model ...
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local minima vs saddle points in deep learning

This is simply trying to convey my intuition, i.e. no rigor. The thing with saddle points is that they are a type of optimum which combines a combination of minima and maxima. Because the number of ...
• 136
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Can overfitting occur in Advanced Optimization algorithms?

There is no technique that will eliminate the risk of overfitting entirely. The methods you've listed are all just different ways of fitting a linear model. A linear model will have a global minimum, ...
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Choosing a learning rate

Copy-pasted from my masters thesis: If the loss does not decrease for several epochs, the learning rate might be too low. The optimization process might also be stuck in a local minimum. Loss being ...
• 17.4k
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Why is learning rate causing my neural network's weights to skyrocket?

You might find Chapter 8 of Deep Learning helpful. In it, the authors discuss training of neural network models. It's very intricate, so I'm not surprised you're having difficulties. One possibility (...
• 331

Hmmm, I am little perplexed by your question. In gradient boosting, we do use the residuals. The residuals are the gradients. You can check my simple implementation of gradient boosting. This is ...

Is Gradient Descent central to every optimizer?

According to the title: No. Only specific types of optimizers are based on Gradient Descent. A straightforward counterexample is when optimization is over a discrete space where gradient is undefined. ...
• 8,509

Why is my generator loss function increasing with iterations?

I think that there are several issues with your model: First of all - Your generator's loss is not the generator's loss. You have on binary cross-entropy loss function for the discriminator, and you ...
• 2,080

Is reseating passengers a reinforcement learning problem?

Reinforcement learning is more about interacting with an environment, and while this could be posed as an RL problem, I think using Global Optimization would be a more direct approach. Essentially ...
• 2,341
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Mathematical formulation of Support Vector Machines?

Your understandings are right. deriving the margin to be $\frac{2}{|w|}$ we know that $w \cdot x +b = 1$ If we move from point z in $w \cdot x +b = 1$ to the $w \cdot x +b = 0$ we land in a ...
• 1,144
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How many times is backprop used in epoch?

It depends on the type of gradient descent or respectively your batch size: One epoch means that your neural net (NN) has applied the forward pass on all examples of your training data, i.e. it has "...
• 4,817

Difference between RMSProp and Momentum?

Optimizers evolved with small Fix/Improvement on the previous one. So, if you will read in sequence, you will have a better understanding. In this context, RMSProp was a fix on Adagrad and it was an ...
• 5,144
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Is it possible to get worse model after optimization?

Is it possible that after running the optimization my score won't get better (and even worse?) ? Yes, theoretically, by pure luck, it is possible that your initial guess, before optimization of hyper-...
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Running huge datasets with R

Although your question is not very specific so I'll try to give you some generic solutions. There are couple of things you can do here: Check sparseMatrix from Matrix package as mentioned by @Sidhha ...
• 406

Choosing a learning rate

Learning rate , transformed as "step size" during our iteration process , has been a hot issue for years , and it will go on . There are three options for step size in my concerning : One is related ...
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