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Try Rectification Improve the features available to your model, Remove some of the NOISE present in the data. In audio data, a common way to do this is to smooth the data and then rectify it so that the total amount of sound energy over time is more distinguishable. # Rectify the audio signal audio_rectified = audio.apply(np.abs) You can also calculate ...


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In regression, the goal is to approximate a function $f: \mathcal{I} \rightarrow \mathbb{R}$, so $f(x) \in \mathbb{R}$. In other words, in regression, you want to learn a function whose outputs can be any number, so not necessarily just a number in the range $[0, 1]$. You use the sigmoid as the activation function of the output layer of a neural network, for ...


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It isn't too surprising to see behaviour like this, since you're using $\mathrm{ReLU}$ activation. Here is a simple result which explains the phenomenon for a single-layer neural network. I don't have much time so I haven't checked whether this would extend reasonably to multiple layers; I believe it probably will. Proposition. In a single-layer neural ...


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I report three definitions of machine learning (ML) and I also explain that ML can be divided into multiple sub-tasks or sub-categories in this answer. However, it may not always be clear why classification, regression, or clustering can be considered machine learning tasks or can be solved with ML algorithms/programs, so let me explain why these tasks can ...


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I'm sure the biases are initially initialized to zero but I don't know how the weights are handled. Looking at the Dense layer docs: by default Dense layers biases are initialized with zeros (bias_initializer='zeros') and weights are initialized with Glorot uniform (kernel_initializer='glorot_uniform'). ... "unusual" element to point here; I've ...


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Short answer: Yes. Consider a non-linear regression on that dataset. Using a model of degree two, it would fit a quadratic exactly to your perfect data here. But I suppose you're asking about neural networks. You can have neural networks set up that are exactly equivalent to this kind of regression, so even with neural networks, yes you can get this non-...


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In general, multi output models is not that different. I.e. As Raghu mentioned in commentary, you could train separate model for each output. There is even helper module in sklearn for that (MultiOutputRegressor) DecisionTreeRegressor from sklearn allows multiple outputs out-of-the box Any neural network framework allows any number of outputs In your ...


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If you have sold only once or very few items you will need some prior input (domain knowledge). One term for search is intermittent time series. Here is a stored search. When you have many time series, related, and interest in both totals and single series, that is called hierarchical forecasting. One expert is here (the author of that blog was the founder ...


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It depends on the complexity of your problem. $\mathbb{R}^2 \rightarrow \mathbb{R}^1$ looks simple, but I can give you some nonsense complicated examples that need a deep network. So, the complexity of the problem sets the number of layers and neurons. The kind of problem will determine the architecture of your network (if it needs memory or not). In most ...


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So generally, when you seperate your training data to 80%-20% then you fit method should get 2 x,y. better to call them x_train,y_train, x_val, y_val or something similar. Now its important you do the split before entering the fit, and not do it for each epoch or something alike. Once you do that and the fit method should be something like: def fit(self, ...


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I'm not aware of a direct way for finding the best NN architecture for a given task, but the recommended way, as far as I know, is to devise a network that can overfit the training data, and then apply regularization on top of it. That way, you can be almost sure you're not underfitting/underperforming due to network capacity.


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