I got a solution for my model, how could I know whether it is feasible in CBC?
Thank you very much!
Related
This is a question on nomenclature. In complexity theory of algorithms, there is a name for a test case constructed specifically to make the algorithm fail. I had that word in my head, but can't recall it for the life of me now. I'm hoping someone here can help. What is the word for an input that is incredibly unlikely to occur in the real world but someone who knows the algorithm can construct to make it fail or perform badly?
You may be thinking of an adversary:
The idea is that an all-powerful malicious adversary pretends to choose an input for the algorithm. When the algorithm wants looks at a bit, the adversary sets that bit to whatever value will make the algorithm do the most work.
The word I was looking for was "pathological case". When reading about the Cauchy distribution (https://en.wikipedia.org/wiki/Cauchy_distribution), I chanced upon it again seeing it described as a "pathological distribution".
I am trying to develop a model which detects the items that are picked up by a user from a basket. Is this achievable using Tensorflow? My doubt is since the basket would contain the same items the user picks up (say fruits), is it possible to report the product that in the user's hand(the products that are picked up by the user) in real-time, rather than the items in the basket? Please advice on what would be a good starting point to achieve this.
I have read and watched general object detection methods using Tensor Flow and various models but nothing seems to deal with a similar solution or I am unable to co-relate. If there are any tutorials on achieving, the links for the same would be even more helpful. Thanks in advance. Please bear with me if my question is naive, I am still a newbie at ML and Tensorflow.
It is achievable using Tensorflow. First consider how you would approach the problem. Since you need to distinguish between objects inside basket you will need ANN with some basic classification and object detection.
Here are some links to get you started:
https://www.tensorflow.org/tutorials/keras/basic_classification
https://www.edureka.co/blog/tensorflow-object-detection-tutorial/
Good luck!
While I am comfortable with optimization problems in Python (and 'R' for that matter) I am curious to know if it can be done in WEKA.
I have X,Y coordinates of several routes and I need to optimize the best overall solution.
Any help would be appreciated...
Weka is a machine learning, not optimization package. While you could try to predict the optimal solution using the machine learning algorithms in Weka, there's nothing to check that a prediction is the optimal solution or even a solution at all.
Definitely sounds to me like you're trying to use the wrong tool for the job.
I am a university student and trying to write a program to solve 1-D Schrodinger's equation with some kinda of potential equations.
I am not a CS major so really have no clue to start.
I did research online but didn't find thing that is suitable for entry level :( The only thing I understand now is that I probably need to use some algorithm solving differential equations
Could anyone give me some suggestions or references on how I could start? For example, how to transform the physical problem into computer science program and what kinda algorithm I should look for?
I am interested in computational so trying on this HARD problem ;)
Thank you all!
go to www.google.com
search for :how can i solve Schrodinger in matlab
go to here
Return stackoverflow
i am doing my final year proj in fingerprint authentication. i 've completed binarization, thinning, minutiae extraction. i am doing it in matlab. i am stuck with the verification. i don't know how to perform verification. so please help me out to solve this problem. i would be grateful to ppl who help .. thanks in advance. please help me out with code for verification or with algorithm atleast... thanks a lot...
The usual way is to use some form of statistical classifier. The most simple is Bayesian (which I believe won't do for fingerprints). In biometry most popular ones are GMM (Gaussian Mixture Model) or HMM (Hidden Markov Model). Another approach is to use neural network as a classifier.
I can't help you with writing your own verification, but i did fingerprint verfication in the past with Griaule Fingerprint SDK. I hope this helps a bit.