Are there situations where we can use either "say" or "tell", but not both, in a sentence? Consider the following examples:
What is the difference between 'tell' and 'say'
I am using ring modulator for DSB-SC  am modulation in a transmitter circuit. Can anyone please tell me about more DSB SC modulator for am modulation? I want to know which one to use where? *.xhtml url-pattern causes java.lang.NullPointerException at com.sun.faces.context.flash.ELFlash.loggingGetPhaseMapForReading FFMPEG video from images comes out same (yet incorrect) duration regardless of frame rate Use dynamic variable names in JavaScript Appending to an ObjectOutputStream How do I contact other users? How can I contact another user, if that user has not specified any contact details in their profile? What is the method to make files in a USB Flash Drive undeletable? Does these method work with USB 3.0 interface?  (I have surveyed some Flash Drive manufacturers, and I only see undeletable files for USB 2.0 drives, and not 3.0 drives. One manufacturer claimed that USB 3.0 cannot do undeletable file, so I am curious whether the claim is correct...) Interprocess communication in .NET I have a core .NET application that needs to spawn an abitrary number of sub processes. These processes need to be able to access some form of state object in the core application.  What is the best technique? I'll be moving a large amount of data between processes (Bitmaps), so it needs to be fast. TL:DR: Is it ever a good idea to train an ML model on all the data available before shipping it to production? Put another way, is it ever ok to train on all data available and not check if the model overfits, or get a final read of the expected performance of the model?    Say I have a family of models parametrized by $\alpha$. I can do a search (e.g. a grid search) on $\alpha$ by, for example, running k-fold cross-validation for each candidate.   The point of using cross-validation for choosing $\alpha$ is that I can check if a learned model $\beta_i$ for that particular $\alpha_i$ had e.g. overfit, by testing it on the "unseen data" in each CV iteration (a validation set). After iterating through all $\alpha_i$'s, I could then choose a model $\beta_{\alpha^*}$ learned for the parameters $\alpha^*$ that seemed to do best on the grid search, e.g. on average across all folds.  Now, say that after model selection I would like to use all the the data that I have available in an attempt to ship the best possible model in production.  For this, I could use the parameters $\alpha^*$ that I chose via grid search with cross-validation, and then, after training the model on the full ($F$) dataset, I would a get a single new learned model $\beta^{F}_{\alpha^*}$   The problem is that, if I use my entire dataset for training,  I can't reliably check if this new learned model $\beta^{F}_{\alpha^*}$ overfits or how it may perform on unseen data. So is this at all good practice? What is a good way to think about this problem? Can I look at the upgrade log after a distribution upgrade? I've upgraded to Ubuntu 12.04 and did it . I didn't look at the "packages to remove" section before clicking next - I've done Ubuntu upgrades many times and assumed it was just the normal set of obsolete library packages etc. But I do tend to watch the terminal text go by and see what it's doing, and I saw it removing a number of packages I wanted. I guess these are packages that aren't on the CD that can't co-exist with the new Ubuntu.  Anyway, I want to find which packages have been removed. So does the update log get saved somewhere?  Then I can start doing some grepping on it and then reinstall the packages I did want. I am so confused by, for example, "People love their life or lives." Evaluate $ 1 + \frac{1}{3}\frac{1}{4}+\frac{1}{5}\frac{1}{4^2}+\frac{1}{7}\frac{1}{4^3}+\dots$ Evaluate $$ 1 + \frac{1}{3}\frac{1}{4}+\frac{1}{5}\frac{1}{4^2}+\frac{1}{7}\frac{1}{4^3}+\dots$$  All i could do was to see that $$\frac{1}{3}=\frac{1}{2.1+1},\frac{1}{5}=\frac{1}{2.2+1},\frac{1}{7}=\frac{1}{2.3+1},\dots$$  SO, we should be able to write   $$ 1 + \frac{1}{3}\frac{1}{4}+\frac{1}{5}\frac{1}{4^2}+\frac{1}{7}\frac{1}{4^3}+\dots=1+\sum_{n=1}^{\infty}\frac{1}{2n+1}.\frac{1}{4^n}$$  This is what i could simplify the given question to...   But, this is doing me no good.. I could not go any further..  I would be thankful if some one can help me to clear this.  I would request users who are trying to help me to just give necessary hints but not post it as answer.  Thank You. For some reason my Steam just broke today after working perfectly since I installed it a few days ago.  It was originally installed via the deb from the Steam site. So I uninstalled that using the Ubuntu Software Center. Then I installed steam-launcher. When I run it from the menu it does nothing at all. When I go through terminal it gives this message, no error:  $ steam/usr/bin/steam:  line 196: /home/andrew/.local/share/Steam/steam.sh: Success   So I purge-removed it and removed the .steams directory from my home directory but on re-installation its the same story.  I am using Xubuntu 12.10 64bit.  It appears that steam .local/share/Steam/steam.sh is blank. What is it meant to have in it? I am so confused with how reinstalling it doesn't fix it. Finding joint density of dependent variables I've got two random variables, $x$ and $y$, where $x=u(y)$ and $y=v(x)$. How do I find the joint density of the two variables?   The eventual purpose is to find the integral  $\int^a\int^b f(x,y) dydx$,  where $f(.)$ is said joint density function.  This question is related () but I'm still a bit confused by the answer. I am indeed effectively looking for $Prob(x \leq a, y \leq b)$ but don't understand how I can get there without knowing the PDF first.  Any advice is much appreciated. Thanks a lot in advance! From time to time I come up with ideas of actions I'd like to have available in the context menu of Nautilus. How do I add them there? Is there something like a Thunar UCA plugin for Nautilus? Does Windows Defender update if Windows Update is set to manual? apache2 mod_rewrite redirect without http body Subfields of $\mathbb{Q}(\sqrt{2},\sqrt{3})$ Suppose one doesn't know any Galois theory, but they are familiar with the basic theory of field extensions. Then how would one justify the following picture?    I'm pretty sure they're just saying these are the known subfields, and there could be more but they just write down those ones (which seems weird, which is why I'm asking). Or have they deduced that this is a comprehensive list of subfields just by using basic theory? This occurs on page 573 of Dummit and Foote's Abstract Algebra. This is a small exercise that I just can't seem to figure out. When I see it I'll probably go 'ahhh!', but so far I haven't made any progress.  I'd like to prove that any linear functional $\phi$ on $\ell^\infty$ that is positive, i.e. for any $(x_n) \in \ell^\infty$ such that $x_n \geq 0$ for all $n \in \mathbb{N}$ we have $\phi((x_n)) \geq 0$, is a bounded linear operator and has $\|\phi \| = \phi((1,1,...))$.  Nothing I have tried worked so far, so any hints would be appreciated, thanks! I am struggling to understand zero inflated distributions. What are they? What's the point?  If I have data with many zeroes, then I could fit a logistic regression first calculate the probability of zeroes, and then I could remove all the zeroes, and then fit a regular regression using my choice of distribution (poisson e.g.).  Then somebody told me "hey, use a zero inflated distribution", but looking it up, it does not seem to do anything differently than what I suggested above? It has a regular parameter $\mu$, and then another parameter $p$ to model the probability of zero? It just does both things at the same time no?