6 votes
Accepted

What are "development test sets" used for?

In machine learning, you normally split your data into 3 parts (80-10-10%). The first part (80% of your initial data) is for the training of your ML model: this is known as the training dataset. The ...
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6 votes
Accepted

Do we need automatic hyper-parameter tuning when we have a large enough dataset?

Unfortunately, even with large amounts of training data, hyperparameter choices can strongly influence the performance of a trained model. What you can usually drop when you have large amounts of ...
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  • 23.9k
5 votes

Can people use neural networks without providing the set of training data?

You cannot train a neural network without training data. It would be like training a football player without making him/her play/watch football or anything that resembles football: it's simply not ...
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  • 33.8k
4 votes
Accepted

What happens to the training data after your machine learning model has been trained?

In many cases, a production-ready model has everything it needs to make predictions without retaining training data. For example: a linear model might only need the coefficients, a decision tree just ...
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  • 291
4 votes
Accepted

How do I select the (number of) negative cases, if I'm given a set of positive cases?

Short answer To select the proper dataset to construct, you should first figure out a metric to use to compare, and then select the dataset construction that gives the better metric. There is no ...
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  • 1,230
4 votes
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How can I estimate how many photos I need to train ResNet-50 for image classification?

What you want to calculate/estimate is known as the sample complexity in computational learning theory. If you knew the VC dimension of the neural network, you may be able to estimate the sample ...
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3 votes

Do we need automatic hyper-parameter tuning when we have a large enough dataset?

You don't NEED a hyperparameter tuner, but it can help in various situations. For example, if your model is not training well, perhaps using a tuner can help. It's hard to say in which ...
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3 votes
Accepted

Why "large set of training data" is needed in Neural Network AI training?

You are not training the model to give the optimum result for one input; You want the model to produce the minimum loss for the whole population of samples that model may be given. The inputs are only ...
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  • 406
2 votes
Accepted

Can people use neural networks without providing the set of training data?

Neural networks are trained by using pairs of example input/output vectors that they learn to associate and can generalise from. In that sense, they always need training data. For supervised learning, ...
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2 votes
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How to source training data in ML for information security?

You have implicitly assumed that supervised learning is being used, given the assumption that labels are needed. But this might lead to the following potential problems: Log file data tends to be ...
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  • 391
2 votes

How to split data for meta-learning?

I assume in your case what you need to be doing is to collate your 3 datasets together - these would form the training dataset, and then leave the testing dataset aside. During Meta-Training, the code ...
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  • 131
2 votes
Accepted

During neural network training, can gradients leak sensitive information in case training data fed is encrypted (homomorphic)?

It will recover the encrypted inputs. The algorithm starts with dummy data and dummy labels, and then iteratively optimizes the dummy gradients to be close as to the original. This makes the dummy ...
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  • 314
2 votes
Accepted

What is the effect of training a neural network with randomly generated fake data that satisfies certain constraints?

This is not advisable. If you train your model with random data your model is not learning anything useful, because there is no information to gain from those examples. Even worse it may (and likely ...
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2 votes
Accepted

Does the ANN's training data include the proper output for every neuron?

Does the ANN's training data include the proper output for every neuron? The short answer is: no (not usually or directly). The long answer is that you can train neural networks in different ways. ...
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1 vote

Given a dataset of people with and without cancer, should I split it into training and test datasets such that the same person is not in both?

we are recognizing the disease, not the person. If you're training a computer vision model with only images and no auxiliary information then a randomized sampling should be enough to prevent the ...
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1 vote

Is binary classification using CNN possible if the training data only consists of one class?

While it won't work as you've possibly imagined it, you might find that implementing it as an autoencoder will allow you to train on one class and then identify things that are "not that." ...
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1 vote

Is creating dataset only by augmentation a bad practice?

Data augmentation is usually rotating, cropping and translating images. And this makes sense if your network could meet these kind of images. If I take a digit recognition like LeNet, it is useless to ...
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  • 211
1 vote

Why not make the training set and validation set one if their roles are similar?

I think this is best explained using an analogy. Also you seen to have the misconception that you don't tune hyper-parameters for training data. You want to increase the accuracy of the training set ...
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  • 1,344
1 vote

Why not make the training set and validation set one if their roles are similar?

Idea is to optimize with regards to unseen data in each step in order to avoid overfitting and data leakage so that the final network will be most generalizable to novel data. First, you initialize ...
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1 vote

Can people use neural networks without providing the set of training data?

In case the question is if NNs can be trained without data, as pointed by others, the answer is negative - any training by definition involves the use of data in some way - supervised, semi-supervised,...
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1 vote

What is the effect of training a neural network with randomly generated fake data that satisfies certain constraints?

If you add fake samples to the training set, your Neural Network learns new dataset that you just made, your fake samples are estimations so you add noise to your training set. you can use Leave one ...
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