让我们熟悉一下导数的几个等价符号。给定y = f(x)，其中x和y分别是函数f的自变量和因变量。以下表达 式是等价的： f ′(x) = y′ = dy dx = df dx = d dxf(x) = Df(x) = Dxf(x), (2.4.2) 其中符号 d dx和D是微分运算符，表示微分操作。我们可以使用以下规则来对常见函数求微分： • DC = 0（C是一个常数） • Dxn = nxn−1（幂律（power 为了微分一个由一些常见函数组成的函数，下面的一些法则方便使用。假设函数f和g都是可微的，C是一个 常数，则： 常数相乘法则 d dx[Cf(x)] = C d dxf(x), (2.4.3) 加法法则 d dx[f(x) + g(x)] = d dxf(x) + d dxg(x), (2.4.4) 乘法法则 d dx[f(x)g(x)] = f(x) d dx[g(x)] + g(x) d dx[f(x)]

impact when both are used together? We have four options: none, quantization, clustering, and both. We would need to train a model with each of these four options to make an informed decision. Blessed with

limited by the user’s bandwidth, and the memory available might be limited too. Let’s see what our options are: 1. The embedding table is too large on-disk: We can use a smaller vocabulary, and see if the

5,000 labels and 10,000 steps when compared to the baseline). Now, there can be a few different options available to us, based on what we want: 1. We only care about reaching the accuracy goal of 80%:

(Dense API) with softmax activation (using Activation module) function. Keras also provides options to create our own customized layers. Customized layer can be created by sub-classing the Keras.Layer