# Tag Info

## Hot answers tagged training-datasets

6

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 second part (10%) is the development set (or dataset), aka validation set. This is used as measuring your performance with various hyperparameters (e.g. in ...

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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 possible. The definition of training/learning in machine learning strictly requires data. You can train a neural network in different ways (e.g. supervised or ...

4

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 single best metric, it depends on the task and your interpretation on what type of error is more important. If you believe it is important that errors should not be ...

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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 data close to the real training data: \mathbf{x}^{\prime *}, \mathbf{y}^{\prime *}=\underset{\mathbf{x}^{\prime}, \mathbf{y}^{\prime}}{\arg \min }\left\|\nabla W^...

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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 huge, and it may be infeasible to label due to the time/expertise required; Then there's the class imbalance problem, in that attack examples are far far rarer ...

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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, a neural network (NN) is trained on a dataset of example inputs and outputs (aka "a labelled dataset") that the user must provide somehow. There are ...

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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 AND validation set at the same time, but the validation set is more important so you want to maximise that accuracy more. Imagine you had a toddler, and you were ...

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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, reward, etc. However, if the question is whether one can obtain something useful I would think about the following use cases: One can use randomly ...

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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 will sample a batch of tasks in each iteration. This batch of tasks will be split into support and query, the algorithm will train on the support and update ...

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