机器学习的本质

1 在CNN中,使用BP算法回溯错误,但是错误的定义是人之前给定好的

2 也就是说,只不过是机器以某种方式(CNN,Feature,Pooling,全连通图)获取到一个随机的概率,然后通过某种方法(BP)让这个概率符合我们给定的概率。

3 当足够接近我们的期望后,就说训练成功

4 训练成功意味着我们得到了一个好的卷积核(过滤器),这个过滤器完全是随机生成的,只要概率OK,则过滤器(二维数组)和各个边上的权重具体什么样无所谓。

5 使用这个向前的方法,可以对未知的输入进行预测,并取得不错的输出。

The above steps train the ConvNet – this essentially means that all the weights and parameters of the ConvNet have now been optimized to correctly classify images from the training set.

When a new (unseen) image is input into the ConvNet, the network would go through the forward propagation step and output a probability for each class (for a new image, the output probabilities are calculated using the weights which have been optimized to correctly classify all the previous training examples). If our training set is large enough, the network will (hopefully) generalize well to new images and classify them into correct categories.

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