1)选择合适的Learning Rate是困难的,太小导致收敛慢,太大阻碍收敛或且导致损失函数在最小值附近波动或发散; 2)预先定义的Learning Rate变动规则不能适应数据集的特性; 3)同样的Learning Rate运用到所有的参数更新(后面的AdaGrad, AdaDelta, RMSProp, Adam为解决此问题而生); 4)最小化高度非凸损失函数的羝问题是:...
Deep Learning的优势不明显,这也是Deep Learning的模型在图像,声音等领域应用效果好的原因,因为这些领域原始特征数据基本含义是一致的,原始特征经过多层抽象后的高层特征是容易理解的,比如,从像素,到边缘,到形状,到类别特征,到物体的概貌,这种逐层抽象的“深度”特征抽象是可以理解,也就是可以“人工地”去设计这种深层...
第一步:加入数据 from keras.datasets import mnist (train_images, train_labels), (test_images, test_labels) = mnist.load_data() #model will learn from training set, then be tested on the test set. #图片通过数字矩阵表示,labels就是1到9的数字列,图像和labels是一一对应的。 2. 第二部:网络...
第一周:深度学习的实用层面(Practical aspects of Deep Learning):http://www.ai-start.com/dl2017/html/lesson2-week1.html 1.1 训练,验证,测试集(Train / Dev / Test sets) 1.2 偏差,方差(Bias /Variance) 1.3 机器学习基础(Basic Recipe for Machine Learning) 1.4 正则化(Regularization) 1.5 为什么正则...
supervised and unsupervised learning. Whereas in supervised learning one has a target label for each training example and in unsupervised learning one has no labels at all, in reinforcement learning one has sparse and time-delayed labels – the rewards. 基于这些收益,agent 必须学会在环境中如何行动...
7.10 深度学习框架(Deep Learning frameworks) 选择深度学习框架需要注意如下事项: 1.便于编程 2.运行速度较快 3.框架开源 7.11 Tensorflow 通常tensorflow框架内置了许多优化函数,如梯度下降,adams等方法。 OVER! 继续冲!!!
Basic Recipe for Machine Learning 训练一个神经网络时遵循的基本原则。 当训练好了最初的神经网络时,首先会问这个算法是否有高偏差high bias,如果存在高偏差,则说明模型在训练集上不能良好的拟合,解决方法是,1.挑选一个新的神经网络,比如,带有更多隐藏层或更多隐藏单元的,或是延长训练时间,让梯度下降法运行更长...
第一周:深度学习的实用层面(Practical aspects of Deep Learning)(t.cn/E9vuVUg) 1.1 训练,验证,测试集(Train / Dev / Test sets) 1.2 偏差,方差(Bias /Variance) 1.3 机器学习基础(Basic Recipe for Machine Learning) 1.4 正则化(Regularization) ...
Deep Learning: Validation Loss Fluctuates Wildly Yet Training Loss is Stable 4 Why does the loss of a CNN decrease for a long time and then suddenly increase? 7 Why there is sudden drop in loss after every epoch? 13 What is causing large jumps in training accuracy and loss between epoc...
If anyone is going to make use of all that training in the real world, and that’s the whole point, what you need is a speedy application that can retain the learning and apply it quickly to data it’s never seen. That’s inference: taking smaller batches of real-world data and quic...