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:
**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](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
* 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](https://arxiv.org/pdf/1502.03167.pdf)
*<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>
# Chapter 4
In this chapter we will learn about object detection and segmentation. The references for this chapter are:
*[https://arxiv.org/pdf/1311.2524.pdf](https://arxiv.org/pdf/1311.2524.pdf)(*Rich feature hierarchies for accurate object detection and semantic segmentation*)
*[https://arxiv.org/pdf/1506.02640.pdf](https://arxiv.org/pdf/1506.02640.pdf)(*You Only Look Once: Unified, Real-Time Object Detection* )
*[https://coursera.org/specializations/deep-learning](https://coursera.org/specializations/deep-learning)(*Deep Learning* course by Andrew Ng)
*[https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf](https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf)(*Fully Convolutional Neural Network for Semantic Segmentation*)
*[https://arxiv.org/pdf/1606.00915.pdf](https://arxiv.org/pdf/1606.00915.pdf)( Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs)
*<https://arxiv.org/pdf/1311.2524.pdf> (*Rich feature hierarchies for accurate object detection and semantic segmentation*)
*<https://arxiv.org/pdf/1506.01497.pdf> (*Faster RCNN Towards Real-Time Object Detection with Region Proposals*)
*<https://www.youtube.com/watch?v=v5bFVbQvFRk>
*<https://arxiv.org/pdf/1506.02640.pdf> (*You Only Look Once: Unified, Real-Time Object Detection* )
*<https://coursera.org/specializations/deep-learning> (*Deep Learning* course by Andrew Ng)
*<https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf> (*Fully Convolutional Neural Network for Semantic Segmentation*)
*<https://arxiv.org/pdf/1606.00915.pdf> ( Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs)
# Chapter 5
In this chapter we will learn about some common CNN architectures (that is, VGG, ResNet, GoogleNet). The references for this chapter are:
*Simonyan, K. and Zisserman, A., 2014, *Very Deep Convolutional Networks for Large-Scale Image Recognition*, [arXiv preprint arXiv:1409.1556](https://arxiv.org/abs/1409.1556)
**Going Deeper With Convolutions,*[https://arxiv.org/abs/1409.4842](https://arxiv.org/abs/1409.4842)
*[*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>
**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](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](https://arxiv.org/pdf/1403.6382.pdf)(*CNN Features off-the-shelf: an Astounding Baseline for Recognition*)
*[https://arxiv.org/pdf/1310.1531.pdf](https://arxiv.org/pdf/1310.1531.pdf)(*DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition*)
*<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*)
# Chapter 9
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:
