6

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 training data is regularisation. If your training examples cover the function space you are learning really well, then it is harder to overfit the training data. ...


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 ...


5

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

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 needs rules/splits, and a neural network needs architecture and weights. The training data isn't required as all the information needed to make a prediction is ...


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 ...


4

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 complexity, but your estimate (bound) may not be very tight anyway (maybe because the estimate of the VC dimension is also not tight). Here is more info about the VC ...


3

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 hyperparameters you would be turning over in your specific model, but for some specific hyperparameters if you choose a bad value your model won't learn or diverge. Take for ...


3

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 considered samples from that population. The more samples you have, if they are drawn i.i.d., the better they can represent that population. That is, you do ...


2

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 ...


2

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 ...


2

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^...


2

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 is) trying to generalize off of your incorrect examples, which will lessen the effect your real examples have. Essentially, you are just dampening your training ...


2

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 ...


2

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. There's supervised, unsupervised, reinforcement learning/training, or even other ways (e.g. online learning). The most common way of training neural networks is ...


1

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 ...


1

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 your network weights randomly. For those weights, training data is unseen so network is optimized with regards to loss function that is calculated using training ...


1

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 ...


1

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 out cross validation technic for evaluating your model.


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