Observer

图床迁移 自从七牛云的测试域名挂了之后，我就把博客的事情抛之脑后，感觉迁图片好麻烦，导致我这个懒癌晚期患者一直无动于衷。直到研究生稀里糊涂地读到了找工作的时候，才猛地想起自己还有个没什么卵用的博客。既然想起来了还是想想办法搞定这个问题。 首先是从七牛云上把我的图片下载下来，根据CSDN一些大佬的代码，尝试用七牛云提供的qrsctl对存储对象进行下载，不过发现基本9成的数据都下载不了，但是url明明可以访问到，无奈只能用script -f result.txt把里面的url信息都输入到一个文本文件里，大概长这个样子： 123456789101112131415Download fail: open ./imgs/image/0001.jpg20: no such file or directoryRequestId: 8mEe0v1YfsFmXwEAimage/0002.jpgINFO: Fetching http://iovip.qbox.me/file/bRxUIFIhkLZ3jkrjALChKKoncigUNACPAwoAACePKOfN2ALizeYo580TSNHb9 ...

以下部分内容转载自LeNet论文的翻译与CNN三大核心思想的解读，卷积神经网络 LeNet-5各层参数详解 相关资料 下面列出的论文都是CNN必读的论文，论文的顺序基本上就是CNN结构演化的历史。 LeNet:《Gradient-Based Learning Applied to Document Recognition》 CNN的开山之作，也是手写体识别经典论文。 AlexNet:《 ImageNet Classification with Deep Convolutional Neural Networks 》 ILSVRC-2012 大赛冠军，促进CNN的扛鼎之作，AlexNet是CNN发展史上的一个历史性转折，不能不读。 Inception V1和V3:《Going Deeper with Convolutions》,《Rethinking the Inception Architecture for Computer Vision》,2014年ImageNet大赛冠军,Inception结构的设计很巧妙 VGGNet:《Very Deep Convolutional Net ...

Deep Learning & Art: Neural Style Transfer Welcome to the second assignment of this week. In this assignment, you will learn about Neural Style Transfer. This algorithm was created by Gatys et al. (2015) (https://arxiv.org/abs/1508.06576). In this assignment, you will: Implement the neural style transfer algorithm Generate novel artistic images using your algorithm Most of the algorithms you’ve studied optimize a cost function to get a set of parameter values. In Neural Style Transfer, you ...

Face Recognition for the Happy House Welcome to the first assignment of week 4! Here you will build a face recognition system. Many of the ideas presented here are from FaceNet. In lecture, we also talked about DeepFace. Face recognition problems commonly fall into two categories: Face Verification - “is this the claimed person?”. For example, at some airports, you can pass through customs by letting a system scan your passport and then verifying that you (the person carrying the passport) are ...

Autonomous driving - Car detection Welcome to your week 3 programming assignment. You will learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers: Redmon et al., 2016 (https://arxiv.org/abs/1506.02640) and Redmon and Farhadi, 2016 (https://arxiv.org/abs/1612.08242). You will learn to: Use object detection on a car detection dataset Deal with bounding boxes Run the following cell to load the packages and dependen ...

Residual Networks Welcome to the second assignment of this week! You will learn how to build very deep convolutional networks, using Residual Networks (ResNets). In theory, very deep networks can represent very complex functions; but in practice, they are hard to train. Residual Networks, introduced by He et al., allow you to train much deeper networks than were previously practically feasible. In this assignment, you will: Implement the basic building blocks of ResNets. Put together these buil ...

Keras tutorial - the Happy House Welcome to the first assignment of week 2. In this assignment, you will: Learn to use Keras, a high-level neural networks API (programming framework), written in Python and capable of running on top of several lower-level frameworks including TensorFlow and CNTK. See how you can in a couple of hours build a deep learning algorithm. Why are we using Keras? Keras was developed to enable deep learning engineers to build and experiment with different models very qu ...

Convolutional Neural Networks: Application Welcome to Course 4’s second assignment! In this notebook, you will: Implement helper functions that you will use when implementing a TensorFlow model Implement a fully functioning ConvNet using TensorFlow After this assignment you will be able to: Build and train a ConvNet in TensorFlow for a classification problem We assume here that you are already familiar with TensorFlow. If you are not, please refer the TensorFlow Tutorial of the third week of ...

Convolutional Neural Networks: Step by Step Welcome to Course 4’s first assignment! In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagation. Notation: Superscript [l][l][l] denotes an object of the lthl^{th}lth layer. Example: a[4]a^{[4]}a[4] is the 4th4^{th}4th layer activation. W[5]W^{[5]}W[5] and b[5]b^{[5]}b[5] are the 5th5^{th}5th layer parameters. Superscript (i)(i)(i) den ...

TensorFlow Tutorial Welcome to this week’s programming assignment. Until now, you’ve always used numpy to build neural networks. Now we will step you through a deep learning framework that will allow you to build neural networks more easily. Machine learning frameworks like TensorFlow, PaddlePaddle, Torch, Caffe, Keras, and many others can speed up your machine learning development significantly. All of these frameworks also have a lot of documentation, which you should feel free to read. In thi ...