Here we're going to learn how to install and the basics of Tensorflow, as references we have:
在这里,我们将学习如何安装以及 Tensorflow 的基础知识,作为我们的参考:
*<https://www.tensorflow.org/tutorials/>
*<https://www.tensorflow.org/install/>
# Chapter 2
# 第 2 章
This chapter will tackle the principals of Machine Learning and Neural Networks with emphasis on Computer Vision with Convolutional Neural Networks. The references for the chapter are:
@@ -20,61 +20,61 @@ This chapter will tackle the principals of Machine Learning and Neural Networks
*<http://cs231n.stanford.edu/>
*<http://cs224d.stanford.edu/>
# Chapter 3
# 第 3 章
This chapter will be cover image classification using Deep learning, and why CNNs disrupted the way we do computer vision now. The references for this chapter are:
* Griffin, Gregory and Holub, Alex and Perona, Pietro (2007) Caltech–256 *Object Category Dataset*
* Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J. and Zisserman, *A International Journal of Computer Vision*, 88(2), 303-338, 2010
**ImageNet Large Scale Visual Recognition Challenge*, IJCV, 2015
*<https://wordnet.princeton.edu/>
**What Does Classifying More Than 10,000 Image Categories Tell Us?* Jia Deng, Alexander C. Berg, Kai Li, and Li Fei-Fei
*Olga Russakovsky, Jia Deng et al. (2015) *ImageNet Large Scale Visual Recognition Challenge*, <https://arxiv.org/pdf/1409.0575.pdf>
*[Olga Russakovsky, Jia Deng et al. (2015) *ImageNet Large Scale Visual Recognition Challenge*](https://arxiv.org/pdf/1409.0575.pdf)
* Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton, *ImageNet Classification with Deep Convolutional Neural Networks*, 2012
*<https://arxiv.org/pdf/1311.2901.pdf>
**Going deeper with convolutions* by Christian Szegedy Google Inc. et al
**Deep Residual Learning for Image Recognition*, Kaiming He et al.
*<https://arxiv.org/pdf/1709.01507.pdf>
*The batch norm paper is a really well written paper that is easy to understand and explains the concept in much more detail, <https://arxiv.org/pdf/1502.03167.pdf>
*[*Very Deep Convolutional Networks for Large-Scale Image Recognition*](https://arxiv.org/abs/1409.1556), Simonyan, K. and Zisserman, A., 2014, arXiv preprint arXiv:1409.1556
**Going Deeper With Convolutions,*<https://arxiv.org/abs/1409.4842>
*[*Going Deeper With Convolutions](https://arxiv.org/abs/1409.4842)
**Deep Residual Learning for Image Recognition*, Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Microsoft Research
**Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications*, <https://arxiv.org/abs/1704.04861>
This chapter will discuss transfer learning and how we can take advantage of other people's model training to help us train our own networks. The references for this chapter are:
*<https://arxiv.org/pdf/1403.6382.pdf> (*CNN Features off-the-shelf: an Astounding Baseline for Recognition*)
*<https://arxiv.org/pdf/1310.1531.pdf> (*DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition*)
*[*CNN Features off-the-shelf: an Astounding Baseline for Recognition*](https://arxiv.org/pdf/1403.6382.pdf)
*[*DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition*](https://arxiv.org/pdf/1310.1531.pdf)
# Chapter 9
# 第 9 章
In the last chapter of this book we will learn how to take advantage of parallel cluster of computers in the cloud to accelerate model training. The references for this chapter are: