"Error analysis in machine learning is what debugging is to actual programming."
"Theory deals with in-sample error and out-of-sample error."
"Native speakers can provide valuable feedback on speech and writing, but they may not know why something is incorrect."
"If you want to understand where errors lie, you must look at what is denied."
"Understanding error: the key to refining machine learning models."
"A lot of these games have felt unforced, it's mistakes more than teams outplaying."
"The utterly broken script was bleeding errors with every second of screen time that passed, and stating the conclusion at the end of the analysis is only natural."
"It's interesting to look at the end of games or the moments near when you die and say like those are the mistakes I made."
"Clearly we have some kind of error going on."
"Rarely is it just one singular mistake; it's like a chain of mistakes that cascaded from one."
"When you rewatch your gameplay, you know where mistakes were made."
"It's very useful to use error analysis to decide... what's the most fruitful next step to take."
"Even the mistakes that Daniel is making are the mistakes that tell me you know what this is legit."
"Any attempt we make to describe something that far unexplored is going to be riddled with error, even down to the fundamental semantics."
"All complexities should, if possible, be buried out of sight, and as you see is where everything went wrong."
"The only mistake is the one from which we learn nothing."
"You need to have proper error estimation on every single thing you want to measure."
"The coefficient of determination tells us how much error is eliminated by our regression line."
"George Russell is he making too many mistakes"
"When errors happen it points to a deficiency in the system and it gives you an opportunity to focus on the deficiency and then improve the system as a result."
"Hitting the errors is probably the most important part of the process because you really don't learn anything unless it's broken."
"The mistake must be at the root, at the very basis of human thinking."
"If you hit a problem, there's always the chance that the problem is a bug. But it's much more likely that the problem is because you have made an error."
"Control values falling outside two standard deviations can be considered random errors."
"Learn to read the error messages..."
"That means we did something to break the program."
"The non-human-like errors and vulnerability to adversarial examples are strong indicators that these systems are not actually understanding the data that they process."
"The graph of that error term lye of X minus PI of X looks like the graph of the stock market."
"Gauss said, 'I know I have errors in my astronomical measurements, but I believe my errors are distributed in what we now call a Gaussian curve, and therefore I can still work with them and get an accurate estimate of the values.'"
"In the world of security, the white label error page is not the end, but the beginning of understanding."
"Once you read the error messages, we can easily understand where the problem resides."
"The error in machine learning decomposes into these three terms: the noise, bias, and variance."
"We can see the impact of regularization on the mean squared error."
"This is saying if you use a threshold rule, then what you're doing is guaranteeing that the probability of error is never going to be less than this quantity of specified here."
"The errors that will be available in the reference will be also available same as the rover."
"The difference between output and correct output of neural network is the error."
"For any function in any dimension, the error goes like one over the square root of the number of samples."
"Analyzing what happens if there's a random error during execution or a corrupted data structure is super important for security."
"This type of error analysis helps you break down the error, so attribute the error to different components, which lets you focus your attention on what to work on."
"The student was wrong because the nitric acid is more concentrated than the potassium hydroxide."
"If the number of spelling mistakes is bigger than 3, then our email is spam."
"Another important part of this is to look at things intersectionally, combining things like gender and race at the same time and seeing how those error rates change and how they're different across different intersections."
"If we know what is the error and what is the error location, then we are home free."
"At the end of the epoch, you sum all the errors, then you take the average of it, which is called mean square error."
"The big picture of the bias-variance tradeoff is that what we will look into today is the generalization error."
"Being able to understand how you classify or how you can explain the error in your model is really important, both from a business standpoint and for the person you're training the model with."
"The theory always works. If the application doesn't act like the theory, then you did something wrong."
"Every measurement should be reported as a measured value plus or minus an error value."
"There are many different ways that you can be wrong in machine learning."
"Next time you get an error message in TensorFlow, be patient and read through the error message."
"My theory is that the error at the center is quite a bit smaller than the error closer to the boundary."
"Error messages are actually pretty easy to figure out and they're really easy to fix in your code."
"This right here is the maximum error that you can possibly get, so you have a better feeling about how good this is."
"One of the most powerful tools you can use is to engineer the data to fix whatever problems error analysis has shown up."
"It's very important to actually read the error message and not panic."
"The relative error of the measurement is equal to absolute error over true value."
"Human error is always a symptom of a system that needs to be redesigned."
"If the rate of accumulation of error decreases, or if the rate of accumulated rate increases but the rate of relative error decreases, then we call it a stable algorithm."
"The rule of random errors becomes quite important when we are dealing with a real-life situation."
"We shall be concerned with how accurate our numerical algorithm is, what is the amount of error that is incurred at each step of scientific computation."
"We are an extremely rigorous company when it comes to dissecting our errors."
"The energy of this error signal can be given as integration t1 to t2, e(t)^2 dt."