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Given e.g. $1$M vectors of $1000$ floating points each, where every point in vectors is sampled from a uniform distribution between $-1$ to $1$, how to estimate the minimum network size required between input ($1000$ units), bottleneck (preferably $1$ unit), and output ($1000$ units) which is cable of overfitting the training data perfectly?

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