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Shillings is a financial app that tracks and forecasts personal finances. It offers insights on spending, income, and investments, with tools for budgeting and financial goal-setting, enabling efficient financial management and planning
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Shillings's answer:
Shillings was designed to be free, no financial budgeting journey should come with an extra added cost.
Shillings's answer:
Our primary audience includes anyone and everyone who wishes to make a financial change and track their budgeting/shifts. There is no set age. It's for everyone
Shillings's answer:
What makes us unique is that not many apps have the features we do, not only do you track your finances and set up goals but you are able to see how much you are predicted to be paid due to your shifts, furthermore helping you to never be underpaid again.
Based on our record, Deep playground seems to be more popular. It has been mentiond 26 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Not the parent, but NNs typically work better when you can't linearize your data. For classification, that means a space in which hyperplanes separate classes, and for regression a space in which a linear approximation is good. For example, take the circle dataset here: https://playground.tensorflow.org That doesn't look immediately linearly separable, but since it is 2D we have the insight that parameterizing by... - Source: Hacker News / 4 months ago
For visualisation and some fun: http://playground.tensorflow.org/. - Source: dev.to / 6 months ago
Https://seeing-theory.brown.edu/ https://www.3blue1brown.com/ https://playground.tensorflow.org/. - Source: Hacker News / 10 months ago
There’s an interactive neural network you can train here, which can give some intuition on wider vs larger networks: https://mlu-explain.github.io/neural-networks/ See also here: http://playground.tensorflow.org/. - Source: Hacker News / 12 months ago
This site is worth playing around with to get a feel for neural networks, and somewhat about ML in general. There are lots of strategies for statistical learning, and neural nets are only one of them, but they essentially always boil down into figuring out how to build a “classifier”, to try to classify data points into whatever category they best belong in. Source: about 1 year ago
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