Artificial Neural Networks – Machine Learning algorithms
The conventional-neural-networks is a subclass of neural-networks which have at least one convolution layer. They are great for capturing local information (eg neighbor pixels in an image or surrounding words in a text) as well as reducing the complexity of the model (faster training, needs fewer samples, reduces the chance of overfitting).
See the following chart that depicts the several neural-networks architectures including deep-conventional-neural-networks:
Neural Networks (NN), or more precisely Artificial Neural Networks (ANN), is a class of Machine Learning algorithms that recently received a lot of attention (again!) due to the availability of Big Data and fast computing facilities (most of Deep Learning algorithms are essentially different variations of ANN).
The class of ANN covers several architectures including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) eg LSTM and GRU, Autoencoders, and Deep Belief Networks. Therefore, CNN is just one kind of ANN.
Generally speaking, an ANN is a collection of connected and tunable units (a.k.a. nodes, neurons, and artificial neurons) which can pass a signal (usually a real-valued number) from a unit to another. The number of (layers of) units, their types, and the way they are connected to each other is called the network architecture.
A CNN, in specific, has one or more layers of convolution units. A convolution unit receives its input from multiple units from the previous layer which together create a proximity. Therefore, the input units (that form a small neighborhood) share their weights.
The convolution units (as well as pooling units) are especially beneficial as:
- They reduce the number of units in the network (since they are many-to-one mappings). This means, there are fewer parameters to learn which reduces the chance of overfitting as the model would be less complex than a fully connected network.
- They consider the context/shared information in the small neighborhoods. This future is very important in many applications such as image, video, text, and speech processing/mining as the neighboring inputs (eg pixels, frames, words, etc) usually carry related information.
Convolutional Neural Networks (CNNs) are neural networks with architectural constraints to reduce computational complexity and ensure translational invariance (the network interprets input patterns the same regardless of translation— in terms of image recognition: a banana is a banana regardless of where it is in the image). Convolutional Neural Networks have three important architectural features.
Local Connectivity: Neurons in one layer are only connected to neurons in the next layer that are spatially close to them. This design trims the vast majority of connections between consecutive layers, but keeps the ones that carry the most useful information. The assumption made here is that the input data has spatial significance, or in the example of computer vision, the relationship between two distant pixels is probably less significant than two close neighbors.
Shared Weights: This is the concept that makes CNNs “convolutional.” By forcing the neurons of one layer to share weights, the forward pass (feeding data through the network) becomes the equivalent of convolving a filter over the image to produce a new image. The training of CNNs then becomes the task of learning filters (deciding what features you should look for in the data.)
Pooling and ReLU: CNNs have two non-linearities: pooling layers and ReLU functions. Pooling layers consider a block of input data and simply pass on the maximum value. Doing this reduces the size of the output and requires no added parameters to learn, so pooling layers are often used to regulate the size of the network and keep the system below a computational limit. The ReLU function takes one input, x, and returns the maximum of {0, x}. ReLU(x) = argmax(x, 0). This introduces a similar effect to tanh(x) or sigmoid(x) as non-linearities to increase the model’s expressive power.