超参上，learning rate最重要，推荐了解cosine learning rate，其次是 batchsize 和weight decay。当你的...
2.1 Data dataset 构建基本不会有啥问题，构建好了后需要检查一下 输出的数据读取是否正确，同一个bat...
To better understand how CNNs work, let’s look at an example of CNNs used for video analytics, a process in which CNN-based computer vision models analyze captured video and extract actionable insights. Computer vision is a subfield of both deep and machine learning that combines cameras, ed...
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks（当前对参数利用最有效的CNN，类...
CNNs and Deep Q Learning 前面的一篇博文介绍了函数价值近似,是以简单的线性函数来做的,这篇博文介绍使用深度神经网络来做函数近似,也就是Deep RL。这篇博文前半部分介绍DNN、CNN,熟悉这些的读者可以跳过,直接看后半部分的Deep Q Learning Part。 Generalization...
后,一副图像变成了好多副特征图(feature map)这时候再进行convolution时,该怎么办呢?所以去瞅了瞅CNN的相关论文。 CNN最经典的案例应该是LeNet-5这个数字识别的任务了吧。这里可以看下Yann Lecun大牛网页 http://yann.lecun.com/exdb/lenet/index.html, 以及tutorial: http://deeplearning.net/tutorial/lenet...
CNN主要由Yann LeCun发明,从1989年到1998年,由多篇论文逐步演进到成熟的CNN模型,详细过程可以查看:http://yann.lecun.com/exdb/lenet/index.html 。这个模型的详细介绍见Yann LeCun1998年的论文(附件Gradient-Based Learning Applied to Document Recognition _ lecun-01a.pdf),...
由于Deep Learning 现在如火如荼的势头,在各种领域逐渐占据 state-of-the-art 的地位,上个学期在一门课的 project 中见识过了 deep learning 的效果,最近在做一个东西的时候模型上遇到一点瓶颈于是终于决定也来了解一下这个魔幻的领域。 据说Deep Learning 的 break through 大概可以从 Hinton 在 2006 年提出的用...
Deep learning belongs to the broader family of machine learning methods and currently provides state-of-the-art performance in a variety of fields, including medical applications. Deep learning architectures can be categorized into different groups depending on their components. However, most of them ...
知识增强深度学习及其应用:综述《Knowledge-augmented Deep Learning and Its Applications: A Survey》(上),被骗了,没什么适合现在情况的技术,小老板还建议精读,几十页,被骗了,被骗了,好想大喊:(ノ`Д)ノ.