patterns in backward flow加法门Add gate: gradient distributor,获取上游梯度,不改变任何值,传递分发给相连的分支; Max门Max gate: gradient router,对大的那个输入的本地梯度是1,对小的输入的本地梯度是0,相当于将上游输入的梯度进行了一个路由选择; 乘法门Mul gate: gradient switcher,对输入x的本地梯度是...
https://becominghuman.ai/back-propagation-in-convolutional-neural-networks-intuition-and-code-714ef1c...
Convolutional Neural Networks backpropagation: from intuition to derivation Backpropagation In Convolutional Neural Networks
总体而言,该课程是入门机器学习的绝佳教程,整体难度不大,但概括了机器学习中很多基础的概念。但弊端在于跳过了过多数学推导,导致有些问题讲解得不是很清楚。特别是在第5周课程Neural Networks: Learning中,虽讲解了 BP 算法的轮廓和执行过程,但没有计算图的辅助和数学公式的推导就很难理解,且出现了较多的讹误。因此...
反向传播法其实是神经网络的基础了,但是很多人在学的时候总是会遇到一些问题,或者看到大篇的公式觉得好像很难就退缩了,其实不难,就是一个链式求导法则反复用。如果不想看公式,可以直接把数值带进去,实际的计算一下,体会一下这个过程之后再来推导公式,这样就会觉得很容易了。
【CNN之反向传播】《Backpropagation In Convolutional Neural Networks | DeepGrid》by Jefkine Kafunah http://t.cn/RcVrDXO
Back-Propagation Learning in Neural NetworksArtificial neural networks; Neural computationDenis Mareschal
Back-propagation training of feed-forward neural networks often results in convergence to local minima, especially when multioutput networks and large trai... C Klawun,CL Wilkins - 《Journal of Chemical Information & Modeling》 被引量: 40发表: 1994年 Signal processing in defect detection using b...
As shown in (Rumelhart et al., 1995) this result occurs whenever we choose a probability function from the exponential family of probability distributions.(这一句不会翻译,没看懂结构) 4.抽头延迟线式存储器 也许最合适的将暂时的或循序的信息包含进一个训练场景(training situation)的方法就是将时间域(...
Implementing logic gates using neural networks help understand the mathematical computation by which a neural network processes its inputs to arrive at a certain output. This neural network will deal…