m0_37879266的博客

不管你想做什么，你都要好好的从论文看，而不是单纯的调论文写代码！通过这些学习，你才能真正的对深度学习的发展，模型的优化，进经典的trick有深入的理解！ 做算法，做科研必不可少！时间有限的人可以只看1.3 2.1 2.2 ！（强烈推荐！） ## 1.3 ImageNet Evolution（Deep Learning broke out from here） **[4]** Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "**Imagenet classification with deep convolutional neural networks**." Advances in neural information processing systems. 2012. [[pdf]](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) **(AlexNet, Deep Learning Breakthrough)** :star::star::star::star::star: **[5]** Simonyan, Karen, and Andrew Zisserman. "**Very deep convolutional networks for large-scale image recognition**." arXiv preprint arXiv:1409.1556 (2014). [[pdf]](https://arxiv.org/pdf/1409.1556.pdf) **(VGGNet,Neural Networks become very deep!)** :star::star::star: **[6]** Szegedy, Christian, et al. "**Going deeper with convolutions**." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf) **(GoogLeNet)** :star::star::star: **[7]** He, Kaiming, et al. "**Deep residual learning for image recognition**." arXiv preprint arXiv:1512.03385 (2015). [[pdf]](https://arxiv.org/pdf/1512.03385.pdf) **(ResNet,Very very deep networks, CVPR best paper)** :star::star::star::star::star: #2 Deep Learning Method ## 2.1 Model **[14]** Hinton, Geoffrey E., et al. "**Improving neural networks by preventing co-adaptation of feature detectors**." arXiv preprint arXiv:1207.0580 (2012). [[pdf]](https://arxiv.org/pdf/1207.0580.pdf) **(Dropout)** :star::star::star: **[15]** Srivastava, Nitish, et al. "**Dropout: a simple way to prevent neural networks from overfitting**." Journal of Machine Learning Research 15.1 (2014): 1929-1958. [[pdf]](https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf) :star::star::star: **[16]** Ioffe, Sergey, and Christian Szegedy. "**Batch normalization: Accelerating deep network training by reducing internal covariate shift**." arXiv preprint arXiv:1502.03167 (2015). [[pdf]](http://arxiv.org/pdf/1502.03167) **(An outstanding Work in 2015)** :star::star::star::star: **[17]** Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. "**Layer normalization**." arXiv preprint arXiv:1607.06450 (2016). [[pdf]](https://arxiv.org/pdf/1607.06450.pdf?utm_source=sciontist.com&utm_medium=refer&utm_campaign=promote) **(Update of Batch Normalization)** :star::star::star::star: **[18]** Courbariaux, Matthieu, et al. "**Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to+ 1 or−1**." [[pdf]](https://pdfs.semanticscholar.org/f832/b16cb367802609d91d400085eb87d630212a.pdf) **(New Model,Fast)** :star::star::star: **[19]** Jaderberg, Max, et al. "**Decoupled neural interfaces using synthetic gradients**." arXiv preprint arXiv:1608.05343 (2016). [[pdf]](https://arxiv.org/pdf/1608.05343) **(Innovation of Training Method,Amazing Work)** :star::star::star::star::star: **[20]** Chen, Tianqi, Ian Goodfellow, and Jonathon Shlens. "Net2net: Accelerating learning via knowledge transfer." arXiv preprint arXiv:1511.05641 (2015). [[pdf]](https://arxiv.org/abs/1511.05641) **(Modify previously trained network to reduce training epochs)** :star::star::star: **[21]** Wei, Tao, et al. "Network Morphism." arXiv preprint arXiv:1603.01670 (2016). [[pdf]](https://arxiv.org/abs/1603.01670) **(Modify previously trained network to reduce training epochs)** :star::star::star: ## 2.2 Optimization **[22]** Sutskever, Ilya, et al. "**On the importance of initialization and momentum in deep learning**." ICML (3) 28 (2013): 1139-1147. [[pdf]](http://www.jmlr.org/proceedings/papers/v28/sutskever13.pdf) **(Momentum optimizer)** :star::star: **[23]** Kingma, Diederik, and Jimmy Ba. "**Adam: A method for stochastic optimization**." arXiv preprint arXiv:1412.6980 (2014). [[pdf]](http://arxiv.org/pdf/1412.6980) **(Maybe used most often currently)** :star::star::star: **[24]** Andrychowicz, Marcin, et al. "**Learning to learn by gradient descent by gradient descent**." arXiv preprint arXiv:1606.04474 (2016). [[pdf]](https://arxiv.org/pdf/1606.04474) **(Neural Optimizer,Amazing Work)** :star::star::star::star::star: **[25]** Han, Song, Huizi Mao, and William J. Dally. "**Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding**." CoRR, abs/1510.00149 2 (2015). [[pdf]](https://pdfs.semanticscholar.org/5b6c/9dda1d88095fa4aac1507348e498a1f2e863.pdf) **(ICLR best paper, new direction to make NN running fast,DeePhi Tech Startup)** :star::star::star::star::star: **[26]** Iandola, Forrest N., et al. "**SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size**." arXiv preprint arXiv:1602.07360 (2016). [[pdf]](http://arxiv.org/pdf/1602.07360) **(Also a new direction to optimize NN,DeePhi Tech Startup)** :star::star::star::star:

pycharm完整教程_全套.pdf ，祝大家不再因为Python库掉头发

我把我的师兄师姐师傅们所有给我分享的C/C++工具书籍全部打包了 也就20本左右吧 我反正是学不完了，用来祸害祸害那些聪明的学弟学妹和大佬吧

飞卡时写了三周圆环。第一周没什么收获，第二周写了两套不对称的算法。第三周完成了最终算法 大概成功率80%吧 希望各位能帮到各位学弟（我实在不相信有学妹会去做这个比赛） 1.考虑一边没有丢边，另一边看生长方向设立标志位强行拐进去再检测两边丢边前方没有路设立标志位强行按相同方向拐出来。 2.用前五行来判断，圆环旁边左右宽度不一样，依据这个给一个标志位，给一个固定打角 3.//搜索控制行的宽度 //搜索控制行前面某行的宽度 //如果控制行宽度大于前面某行的宽度 //判断控制行及其后几行是否有边界 //有的话进入圆环

iarformsp430百度云，自己下载。安装简便。内含教程。

数学实验上老师分享的资料和PPT，很全面，足够支撑大家从小白到初级。机器学习的基础部分介绍的比较详细。仅限于MATLAB

初学机器学习常用的MATLAB的一些应用，涉及到基础的数学实验和工程建立。也算是当年帮了自己很大的忙。分享给大家，希望能对大家有帮助。

PWM资料（C代码+文档描述+有关单片机的一些设计） 做电子设计时候搜集的一些资料 希望能帮到大家

本人在学习MSP430f5529当中的一些资料和心得吧，希望能够给大家一些提示和帮助 里面有一些基础的例程