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Binary labels \u2014\u2014 favs, likes and clicks<\/h2>\n\n\n\n

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\u5747\u503c\u5f52\u4e00\u5316<\/h2>\n\n\n\n

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\u5c06\u8be5\u7528\u6237\u7684y\u503c\u90fd\u5199\u62100\u867d\u7136\u53ef\u4ee5\u52a0\u5feb\u5b66\u4e60\u901f\u5ea6\uff0c\u4f46\u662f\u5e76\u4e0d\u5229\u4e8e\u6a21\u578b\u6027\u80fd\u7684\u63d0\u5347\u3002<\/p>\n\n\n\n

\"\"<\/div><\/figure>\n\n\n\n

\u5747\u503c\u5f52\u4e00\u5316\uff0c\u5148\u7b97\u7528\u6237\u6570\u636e\u77e9\u9635\u6bcf\u4e00\u4e2a\u884c\u7684\u5e73\u5747\uff0c\u7136\u540e\u77e9\u9635\u4e2d\u6240\u6709\u6570\u636e\u90fd\u51cf\u53bb\u03bc\u3002<\/p>\n\n\n\n

\u6240\u4ee5\u8ba1\u7b97\u7528\u6237j\u5bf9\u7535\u5f71i\u7684\u8bc4\u4ef7\u9884\u6d4b\u65f6\u8981\u52a0\u4e0a\u03bc\uff08\u56e0\u4e3a\u4e4b\u524d\u51cf\u4e86\u03bc\uff09\u3002<\/p>\n\n\n\n

\u8fd9\u65f6\u5019\u6211\u4eec\u8ba1\u7b97\u8fd9\u4e2a\u6ca1\u6709\u505a\u4efb\u4f55\u8bc4\u4ef7\u7528\u6237\u7684\u9884\u6d4b\u503c\u65f6\u53d1\u73b0\uff0c\u56e0\u4e3a\u8be5\u7528\u6237\u6ca1\u6709\u505a\u4efb\u4f55\u8bc4\u4ef7\uff0c\u6240\u4ee5\u4e0b\u56fe\u7ea2\u6846\u4e2d\u7684\u5f0f\u5b50\u5c31\u662f0\uff0c\u7136\u540e\u52a0\u4e0a\u03bc\uff0c\u5c31\u76f4\u63a5\u9884\u6d4b\u8be5\u7528\u6237\u4f1a\u6253\u4e00\u4e2a\u5e73\u5747\u503c\u03bc\u624d\u662f\u5408\u7406\u7684\u3002<\/p>\n\n\n\n

\"\"<\/div><\/figure>\n\n\n\n

\u534f\u540c\u8fc7\u6ee4\u7684TensorFlow\u5b9e\u73b0<\/h2>\n\n\n\n

\u7136\u540e\u662f\u534f\u540c\u8fc7\u6ee4\u7684TensorFlow\u7684\u5b9e\u73b0\uff0c\u6211\u4eec\u53ef\u4ee5\u7528\u5982\u4e0b\u4ee3\u7801\u81ea\u5b9a\u4e49\u8bad\u7ec3\u5faa\u73af\uff1a<\/p>\n\n\n\n

w = tf.Variable(3.0) # \u8981\u4f18\u5316\u7684\u53c2\u6570\nx = 1.0\ny = 1.0\nalpha = 0.01\n\niterations = 30\nfor iter in range(iterations):\n    with tf.GradientTape() as tape:\n        fwb = w * x\n        costJ = (fwb - y) ** 2 # \u4ee3\u4ef7\u51fd\u6570\n    \n    [dJdw] = tape.gradient(costJ, [w]) # \u68af\u5ea6\uff08\u8ba1\u7b97\u5bfc\u6570\uff09\n    \n    w.assign_add(-alpha * dJdw) # \u8d4b\u503c<\/code><\/pre>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u7136\u540e\u662f\u5bf9\u5e94\u8fd9\u91cc\u7684\u68af\u5ea6\u4e0b\u964d\u7b97\u6cd5\uff1a<\/p>\n\n\n\n
optimizer = keras.optimizers.Adam(learning_rate=1e-1)\n\niterations = 200\nfor iter in range(iterations):\n    with tf.GradientTape() as tape:\n        cost_value = cofiCostFuncV(x, w, b, Ynorm, R, num_users, num_movies, lambda)\n        \n        grads = tape.gradient(cost_value, [x, w, b])\n        \n        optimizer.apply_gradients(zip(grads, [x, w, b]))<\/code><\/pre>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u67e5\u627e\u76f8\u5173\u7b97\u6cd5<\/h2>\n\n\n\n
\u67e5\u627e\u76f8\u5173\u7b97\u6cd5\u548c\u4e4b\u524d\u4ecb\u7ecd\u7684\u805a\u7c7b\u6709\u4e9b\u7c7b\u4f3c\uff0c\u6216\u8005\u8bf4\u5c31\u662f\u805a\u7c7b\u7684\u4e00\u4e2a\u5e94\u7528\u3002<\/p>\n\n\n\n
\u67e5\u627e\u5c31\u662f\u627e\u4e00\u4e2a\u4e0ex(i)<\/sup>\u76f8\u8fd1\u7684x(k)<\/sup>\u3002\u627e\u6700\u5c0f\u8ddd\u79bb\u7684\u4e00\u4e2a\u5411\u91cf\u3002<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u6700\u540e\u662f\u534f\u540c\u8fc7\u6ee4\u7684\u5c40\u9650\u6027\u76f8\u5173\uff1a<\/p>\n\n\n\n
\u4e00\u4e2a\u662f\u51b7\u542f\u52a8\u7684\u95ee\u9898\uff0c\u5c31\u662f\u5982\u4f55\u5bf9\u7528\u6237\u6253\u5206\u5c11\u7684\u9879\u76ee\u6392\u540d\uff1f\u8fd8\u6709\u5982\u4f55\u5411\u65b0\u7528\u6237\u5c55\u793a\u5408\u7406\u5185\u5bb9\uff1f<\/p>\n\n\n\n
\u5e38\u89c1\u7684\u505a\u6cd5\u662f\u4f7f\u7528\u9879\u76ee\u6216\u8005\u7528\u6237\u7684\u4fa7\u9762\u4fe1\u606f\u3002<\/p>\n\n\n\n
\u5bf9\u4e8e\u9879\u76ee\u6765\u8bf4\u5c31\u662f\u6839\u636e\u7c7b\u578b\u3001\u7535\u5f71\u660e\u661f\u3001\u5de5\u4f5c\u5ba4\u7b49\u7b49\u3002<\/p>\n\n\n\n
\u7528\u6237\u6765\u8bf4\u5c31\u662f\u4eba\u53e3\u7edf\u8ba1\uff08\u5e74\u9f84\u3001\u6027\u522b\u3001\u4f4f\u5740\uff09\u3001\u8868\u8fbe\u504f\u597d\u7b49\u7b49\u3002<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n","protected":false},"excerpt":{"rendered":"
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