Answer1215

摘要： Training an algorithm on a very few number of data points (such as 1, 2 or 3) will easily have 0 errors because we can always find a quadratic curve t 阅读全文

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摘要： In this section we examine the relationship between the degree of the polynomial d and the underfitting or overfitting of our hypothesis. We need to d 阅读全文

摘要： Just because a learning algorithm fits a training set well, that does not mean it is a good hypothesis. It could over fit and as a result your predict 阅读全文

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摘要： First, pick a network architecture; choose the layout of your neural network, including how many hidden units in each layer and how many layers in tot 阅读全文

摘要： section img { --aspect-ratio: calc( 4 / 3); --height: 30vmin; --width: calc(var(--height) * var(--aspect-ratio)); height: var(--height); width: var(-- 阅读全文

摘要： As you can see "Characters" take two params, page & filter. Here use two alias: allChars: characters fullNmae: name. Alias is also useful when you nee 阅读全文

摘要： Gradient checking will assure that our backpropagation works as intended. We can approximate the derivative of our cost function with: epsilon = 1e-4; 阅读全文

摘要： When consuming asynchronous selectors in Recoil, you're going to need to tell React what to render while the API is fetching its data. One way to solv 阅读全文