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title: Convolutional Neural Networks
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Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs. ConvNets have been utilized extensively to power vision in robots and self driving cars.

### Why Convolutional Neural Network

CNNs are biologically inspired models on how mammals visually perceive things. When we see something a layer of neurons are activated in our brain. The very same concept is working here too. The birth of CNN is inspired by Local connection, layering, spatial invariance. Compared to the similar size of feed forward neural network the CNN only require much fewer connections and parameters, hence they are easier to train and also the time consumption is less. CNN is effective for both the high level and low level features in dataset. Another important factor of CNN is the depth of the layers.

### Suggested links :
 - Stanford CS231n [Lecture 5 Convolutional Neural Networks](https://www.youtube.com/watch?v=bNb2fEVKeEo)
 - Stanford CS231n [Lecture 9 CNN Architectures](https://www.youtube.com/watch?v=DAOcjicFr1Y&t=2384s)
 - Udacity Deep learning : [Convolutional netwoks](https://www.youtube.com/watch?v=jajksuQW4mc)
 - Andrew Ng's DeepLearning.ai: [Convulational Neural Networks](https://www.coursera.org/learn/convolutional-neural-networks/)


### E-Texts : 
 - Intro to Machine Learning (http://ciml.info/)
 - Understanding Neural Networks (http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf) 
