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1

Image channels have nothing to do with machine learning, they are just part of computer image processing. A channel is a number per pixel. So most colour images are stored with red, green and blue channels, as you probably know. Some images are stored in greyscale with just one white channel. A RGB image is stored like this: pixel 0 red amount, pixel 0 green ...


1

Mean Absolute Error is nothing but the mean of absolute errors. If your model gave $n$ predictions $\{\hat{y}_i\}_{i = 1}^{n}$ against $n$ ground truths $\{y_i\}_{i = 1}^{n}$, then MAE is defines as follows $$MAE_{model} = \dfrac{\sum\limits_{i = 1}^{n} |y_i - \hat{y}_i|}{n} $$. Thus, MAE gives the average amount of error. So, the machine learning model with ...


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Take a look at either of these great posts about negative R2 values. What does negative R-squared mean? When is R squared negative? TLDR is that your model is poorly fit to the data. From looking at the code you attached I would try reducing the number of x features you are using. It is possible that there is multicollinearity or some feature just are not ...


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Also, keep in mind that not just any augmentation of the loss function is a regularization. For example, you can add terms to a loss function that enforce constraints on the solution but do not prevent overfitting nor facilitate generalization.


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All modern frameworks for deep learning (PyTorch, Jax, Tensorflow) support automatic differentation. These operations can be easily implemented. Here I write, how it would look like in PyTorch: class Net(nn.Module): def __init__(self): super().__init__() self.a = nn.Parameter(torch.randn(1)) self.b = nn.Parameter(torch.randn(...


3

Regularization is not limited to methods like L1/L2 regularization which are specific versions of what you showed. Regularization is any technique that would prevent network from overfitting and help network to be more generalizable to unseen data. Some other techniques are Dropout, Early Stopping, Data Augmentation, limiting the capacity of network by ...


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There are better methods for selecting most important features in supervised setting. Assuming they are not an option, or you're simply interested in PCA: Say you originally had 100 features and you applied PCA and first 10 PCs explains the 95 % of ratio. After applying PCA, you can calculate linear correlations between top 10 PCs and original features. I ...


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The loss is $$\mathcal{L}=\sum_{i=1}^{N} \ell\left(y_{i}, f\left(\mathbf{x}_{i}\right)\right) \equiv \sum_{i=1}^{N} \exp \left(-y_{i} f\left(\mathbf{x}_{i}\right)\right),$$ which can also be written as follows $$\mathcal{L} = \sum_{i=1}^{N} e^{-y_{i} f\left(\mathbf{x}_{i}\right)} \tag{1}\label{1}$$ The important thing to note here is the $-$ in the exponent, ...


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Use the benchmarked algorithms or research papers will be a good start. Addition to that use the open sourced Bert GPT 2 like architectures is a good start.


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You can claim to use a real-world dataset, you would just need to specify that some values were interpolated. Do you have to have the inter-mediate values though? By the looks of it, each "region" was only measured every 2 hours, so I would just keep it that way and just have the resolution be 2 hours. It doesn't have to be hourly, and probably ...


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Look for padding. There are even versions of it: pre-padding, post-padding. https://stackoverflow.com/questions/46298793/how-does-choosing-between-pre-and-post-zero-padding-of-sequences-impact-results


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A recurrent neural network (RNN, specifically either an LSTM or GRU) will work well for variable length sequences like you’ve described. Assuming the order of the sequence is meaningful (I.e. you can’t just break up the sequence into individual inputs and associated target value) an RNN model will learn how the sequence of inputs maps to the sequence of ...


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I often check one of these: https://www.paperswithcode.com/ http://www.arxiv-sanity.com/ https://www.youtube.com/c/YannicKilcher/videos https://www.reddit.com/r/MachineLearning/ And, of course, Twitter :)


3

Essentially, any data you use to train or develop the model shouldn't be used as test data. In principle, "unseen" data gives a good estimate for the generalisation performance of the model; but this is only valid if the data really is unseen and hasn't been used in the model development process. If you've been tuning a model to increase its ...


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 ...


4

Yes, you can fix (or freeze) some of the weights during the training of a neural network. In fact, this is done in the most common form of transfer learning (which is described here). I don't know exactly how this affects learning in general. In transfer learning, this is definitely beneficial, as we are freezing the weights that are associated with the ...


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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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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 ...


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