Python Web开发实战.mobi

【实例简介】

零基础学python（明日科技）源代码

Django验证码示例(用于登陆验证)

【实例简介】(最新)强化学习导论：Reinforcement learning - An introduction        2020英文版  作者：Richard S. Sutton and Andrew G. Barto       强化学习权威之作

【实例简介】

通过岭回归实现 数据标签的预测

【实例简介】

《生成对抗网络入门指南》随书源码

LSTM入门学习，正弦波序列预测         for i, input_t in enumerate(input.chunk(input.size(1), dim=1)):             h_t, c_t = self.lstm1(input_t, (h_t, c_t))             h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))             output = self.linear(h_t2)  # output.shape:[batch,1]             outputs = [output]  # outputs.shape:[[batch,1],...[batch,1]], list composed of n [batch,1],         for i in range(future):  # if we should predict the future             h_t, c_t = self.lstm1(output, (h_t, c_t))             h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))             output = self.linear(h_t2)  # output.shape:[batch,1]             outputs = [output]  # outputs.shape:[[batch,1],...[batch,1]], list composed of n [batch,1],         outputs = torch.stack(outputs, 1).squeeze(2)  # shape after stack:[batch, n, 1], shape after squeeze: [batch,n]         return outputs

【实例简介】