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I have two traffic related datasets. One contains traffic signs and the other traffic lights.
I want to merge the two datasets and train the model to detect both of them.
Will unlabeled traffic-signs from the traffic-light dataset affect the training process and vice versa?
From what I've read so far YOLO also learns contextual information about the objects and that's why this concern.
As you mentioned and as I found there, "YOLO sees the entire image during training and test time so it implicitly encodes contextual information about classes as well as their appearance", but I see that the meaning is that it considers the direct areas around the labels to add their information to the trained network, thus, you will likely only lose the information from the unlabeled items, but it will not impact the labeled items negatively.
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Feed forward neural network do they belong to deep neural network or ANN?
I have a doubt whether to call my frame work as deep neural network or a artificial neural network
FFN refers to neural networks in which information flows into one direction only, as opposed to Recurrent Network for instance where information can flow back form the previous time steps.
An FFN is a type of ANN. "Deep" usually means several layers stacked. A one layer ANN would usually be qualified as "Shallow".
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I came across this question and I couldn't find it in textbooks or the internet. Seems pretty unique.
I guess there would be some comparators and adders involved, but I have no clue where to start.
The first step will undoubtedly be XORing the two bit sets. Then you need to count the number of logical ones in the output. The best method for designing your circuit would be to make a complete analogy of the hack discussed in this question and explained perfectly in its answer by nneonneo. This would result in the optimal tree of adders, rather than relying on sequential counting. The idea is that in each layer you know how to cap the maximum possible sum of a subset of the inputs, and in how many bits it will fit, eliminating the need for a carry bit. The programming approach is designed for 32 bits but easily modifiable for less or more than that.
For more possible algorithms for computing Hamming weight see this link.
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I have a vague sense of the meaning of this term, usually in the context of data structures and algorithms which happen to rely upon swap variables to shuttle data around containers and what not. But I'd like to hear some richer definitions and nuances to people's knowledge of this term. Taking a shot at it myself I'd say doing something in place (interesting subquestion, what verbs can come before inplace? moving inplace? transferring inplace? copying inplace?) is to transfer elements of container data from one memory location to another without recourse to a second copy of the whole container.
"inplace" usually means "with O(1) additional space".
This term is often used to indicate an alternative to some operation that would normally involve some kind of a copying operation. The alternative achieves the same results, but avoids the copying procedure or operation, whatever the case may be.
One example comes from C++. Before the C++11 revision to the language, adding an element to a container could not avoid a copy operation of some kind, which could get expensive when the container has a non-trivial object.
If a completely new class instance is to be added to the container, it was pretty much a foregone conclusion that what ends up happening is: 1) class instance construction, 2) a copy construction, and 3) destruction of the first instance.
C++11 added certain language features that make it possible to avoid copying, with the new class instance ending up getting construct "in place", or "emplace" inside the container.
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I have a "very simple" problem.. When i make a custom report in a filtered view in my Analytics account, the numbers i see on the online dashboard differs from the ones, that are downloaded directly from that report into Excel .csv.. We do cross-domain tracking, and the purpose of the filtered view is to see the domains in the view (basic, suggested by analytics help).
What could be the problem? Do any of you suffer from the same problem? This is very annoying, because we can't trust our numbers..
Thank you in advance,
Adam
Your issue may be sampleing level
samplingLevel=DEFAULT
Optional.Use this parameter to set the sampling level (i.e. the number of sessions used to calculate the result) for a reporting query. The allowed values are consistent with the web interface and include: •DEFAULT — Returns response with a sample size that balances speed and accuracy.
•FASTER — Returns a fast response with a smaller sample size.
•HIGHER_PRECISION — Returns a more accurate response using a large sample size, but this may result in the response being slower.
If not supplied, the DEFAULT sampling level will be used.See the Sampling section for details on how to calculate the percentage of sessions that were used for a query.
There is no way to test if this is your problem, because at this time you cant set sampling level when extracting as CSV nor can you set it in the query explorer. But I wrote an application that will extract information into CSV and allows you to select sampling level. Daimto - Google Analytics Export you can use that to test if this is the problem or not.
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I've come across the phrase multiple times the past month or so, usually when talking about functionality. Is this any different than saying 'built-in', or carry with it any specific connotations?
From dictionary.com:
a. (Computers) to incorporate (a feature) as part of a
system or piece of software or hardware while it is still in
development: The location-tracking service is baked in the new app.
Security features come baked into the operating system.
b. To include
as an inseparable or permanent part: Baked into the price of the
product is the cost of advertising.
Your assumptions are correct. It pretty much means that you don't have to do anything special to install this functionality. It's built in. Already there.
Other variants are:
Rolled in
Built in
Native