Sequence Model-week1编程题1（一步步实现RNN与LSTM）

【Sequence Model-week1编程题1（一步步实现RNN与LSTM）】的更多相关文章

Sequence Model-week1编程题1（一步步实现RNN与LSTM）

一步步搭建循环神经网络将在numpy中实现一个循环神经网络 Recurrent Neural Networks (RNN) are very effective for Natural Language Processing and other sequence tasks because they have "memory". 他们可以读取一个输入 \(x^{\langle t \rangle}\) (such as words) one at a time, 并且通过隐藏层激活…

改善深层神经网络-week1编程题（Initializaion）

Initialization 如何选择初始化方式,不同的初始化会导致不同的结果好的初始化方式: 加速梯度下降的收敛(Speed up the convergence of gradient descent) 增加梯度下降收敛成一个低错误训练(和普遍化)的几率(Increase the odds of gradient descent converging to a lower training (and generalization) error) To get started, run…

改善深层神经网络-week1编程题（Regularization）

Regularization Deep Learning models have so much flexibility and capacity that overfitting can be a serious problem,if the training dataset is not big enough. Sure it does well on the training set, but the learned network doesn't generalize to new ex…

改善深层神经网络-week1编程题（GradientChecking）

1. Gradient Checking 你被要求搭建一个Deep Learning model来检测欺诈,每当有人付款,你想知道是否该支付可能是欺诈,例如该用户的账户可能已经被黑客掉. 但是,反向传播实现起来非常有挑战,并且有时有一些bug,因为这是一个mission-critical应用,你公司老板想让十分确定,你实现的反向传播是正确的.你需要用"gradient checking"来证明你的反向传播是正确的. # Packages import numpy as np from…

Convolutional Neural Network-week1编程题（一步步搭建CNN模型）

Convolutional Neural Networks: Step by Step implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagation. Notation: Superscript \([l]\) denotes an object of the \(l^{th}\)…

Coursera, Deep Learning 5, Sequence Models, week1 Recurrent Neural Networks

有哪些sequence model Notation: RNN - Recurrent Neural Network 传统NN 在解决sequence input 时有什么问题? RNN就没有上面的问题. 注意这里还提到了BRNN 双向RNN的概念. 激活函数 g1 经常用的是tanh, 也有用relu的但是不常用 Backpropagation through time Difference types of RNNs Language model and sequence generatio…

Sequence Model-week1编程题2-Character level language model【RNN生成恐龙名 LSTM生成莎士比亚风格文字】

Character level language model - Dinosaurus land 为了构建字符级语言模型来生成新的名称,你的模型将学习不同的名字,并随机生成新的名字. 任务清单: 如何存储文本数据,以便使用RNN进行处理. 如何合成数据,通过采样在每个time step预测,并通过下一个RNN-cell unit. 如何构建字符级文本,生成循环神经网络(RNN). 为什么梯度修剪(clipping the gradients)很重要? import numpy as np imp…