You may have to understand this concept by knowing three different concepts and they are a. training set b. validation set c. test set Any data-set you have and when you want to apply any algorithms to it you need to split the data-set into the above three. a. training set usually you give around … Read more
If I’m not mistaken, you have two inputs X1, X2, and one target output. For each input consisting, of two numbers X1, X2, the appropriate output (“target”) is given. As a first step, you could sketch the seven data points – just draw the 3 ones and 4 zeroes at the right places on on … Read more
I used to face the imbalanced dataset before. I used the resample function from sklearn, You can read more from here. The function will randomly copy from the minority to upsampling. In my experience, this method only slightly improve the model, I think you should find data on the internet to add into your dataset.
Outputs of a neural network are not probabilities (generally), so that could be a reason that you’re not getting the “1 – P” result you’re looking for. Now, if it’s simple logistic regression, you’d get probabilities as output, but I’m assuming what you said is true and you’re using a super-simple neural network. Also, what … Read more
Check these out in that order 🙂 Natural Language Tool Kit [NLTK] main page(It is in Python),Building a machine learning class around NLTK And Making an actual Application out of it. This should give you a great foundation for building NLP applications(provided your NLP fundamentals are strong)
It is a common missconception to mix concepts of “algorithm”, “method”, “model” and “implementation” of particular ML concepts. Most of things defined in ML community is a model or method, not an algorithm or implementation. Roughly speaking: model is a form of representing actual, real processes in the form of mathematical equations/formulas. One of such … Read more
Something like the following will work: with open(‘resume.txt’, ‘r’) as f_input: print re.findall(r’\b([a-z0-9-_.]+?@[a-z0-9-_.]+)\b’, f_input.read(), re.I) It will display: [‘[email protected]’] But the exact logic can be far more complicated. It all depends on how accurate it needs to be. This will display all email addresses in the text file, just in case there is more than … Read more
Save your model or the weights of your model: model.save(filename) np.save(filename, model.get_weights()) For loading, in the first case: from keras.models import load_model model = load_model(filename) In the second case: #recreate the model then: model.set_weights(np.load(filename)) Then: results = model.predict(batch_of_data)
It is impossible. The lottery numbers are random – actually to be more specific, the system is chaotic. You would require the initial configuration (positions etc) to insane (possibly infinite) precision to be able to make any predictions. Basically, don’t even try it.