Behavior 就不多说了 每个人都会考，每条军规都要准备\n\n\n\nsql 考的比较简单，各种join 各种groupby， 还有一题需要用到rank（） partition by。\n\n\npython 要求我写一个 Gradient Descent 的seudo code，并解释每一步的逻辑。\n给大家吧deep seek 的答案放在这\ndef gradient_descent(X, y, learning_rate=0.01, num_iterations=1000):\n 初始化参数\n m, n = X.shape\n theta = np.zeros(n) 参数向量. 1point3acres\n cost_history = [] 记录损失函数值\n\n for i in range(num_iterations):\n 计算预测值\n y_pred = np.dot(X, theta)\n\n 计算误差. 1point 3acres\n error = y_pred - y. 1point3acres\n. .и\n 计算梯度. 1point3acres.com\n gradient = np.dot(X.T, error) / m\n\n 更新参数. From 1point 3acres bbs\n theta = theta - learning_rate gradient\n.\n 计算损失函数值（可选）\n cost = np.sum(error 2) / (2 m).\n cost_history.append(cost)\n\n return theta, cost_history