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1500 questions
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1 answer

Neural network with flexible number of inputs?

Is it possible to create a neural network which provides a consistent output given that the input can be in different length vectors? I am currently in a situation where I have sampled a lot of audio files, which are of different length, and have to…
Carlton Banks
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How can I get the ImageNet ILSVRC 2012 data used for the classification challenge?

I would like to see if I can reproduce some of the image net results. However, I could not find the data (the list of URLs) used for training / testing in the ILSVRC 2012 (or later) classification challenges. I only found…
Martin Thoma
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4 answers

What is affine transformation in regard to neural networks?

I have been reading a paper recently on Highway Neural Networks and found the following: $y=H(x,W_H)$ $H$ is usually an affine transform followed by a non-linear activation function, but in general it may take other forms. After Googling about…
minerals
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3 answers

Build a tool for manually classifying training data images

I have a large number of images that I need to classify for training a clustering algorithm, and I would like to do so offline (the data is proprietary). Basically, I'd like to build a desktop survey tool that enables me to rapidly place each image…
atkat12
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1 answer

Minimum number of trees for Random Forest classifier

I am searching for a theoretical or experimental estimation of the lower bound for the number of trees in a Random Forest classifier. I usually test different combinations and select the one that (using cross-validation) provides the median best…
gc5
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What's an LSTM-LM formulation?

I am reading this paper "Sequence to Sequence Learning with Neural Networks" http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Under "2. The Model" it says: The LSTM computes this conditional probability by…
Taivanbat Badamdorj
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3 answers

Nested cross-validation and selecting the best regression model - is this the right SKLearn process?

If I understand correctly, nested-CV can help me evaluate what model and hyperparameter tuning process is best. The inner loop (GridSearchCV) finds the best hyperparameters, and the outter loop (cross_val_score) evaluates the hyperparameter tuning…
BobbyJohnsonOG
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2 answers

Neural network obfuscation

Neural networks are trained to minimize some error function over the weights of the neural connections. In some applications, these weights could be considered intellectual property. Is there a way to encrypt these weights and still have an…
Maxwell Johansen
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4 answers

Is t-SNE just for visualization?

I have used the t-SNE algorithm to visualize my high dimensional data. However, I was wondering if this is a practical method for inference?
smw
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How to approach the numer.ai competition with anonymous scaled numerical predictors?

Numer.ai has been around for a while now and there seem to be only few posts or other discussions about it on the web. The system has changed from time to time and the set-up today is the following: train (N=96K) and test (N=33K) data with 21…
Richi W
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7 answers

Career switch to Big Data Analytics

I am a 35 year old IT professional who is purely technical. I am good at programming, learning new technologies, understanding them and implementing. I did not like mathematics at school, so I didn't score well in mathematics. I am very much…
KurioZ7
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feature importance via random forest and linear regression are different

Applied Lasso to rank the features and got the following results: rank feature prob. ================================== 1 a 0.1825477951589229 2 b 0.07858498115577893 3 c 0.07041793111843796 Note that the data set has…
neurite
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Tips for a new data scientist

I am about to start a job in which I will be working with large datasets and will be expected to find trends, etc... I have found lots of resources on where to learn ML and other hard skills and feel that I am (semi) competent on this end. I am…
Hobbes
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How to update bias and bias's weight using backpropagation algorithm

I'm writing my own training algorithm, but I don't know how to set the bias weight. Have I to set bias in any layer? Must the bias weight, be updated in every layer?
miguelote
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5 answers

Convolutional Neural Networks in R

I don't see a package for doing Convolutional Neural Networks in R. Has anyone implemented this kind of algorithm in R?
Hack-R
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