ANN vs CNN vs RNN

Artificial Neural Network (or ANN) Well, it is a group where we have multiple perceptrons or neurons at each layer and is also known as a Feed-Forward Neural network because inputs are processed only in the forward direction. This type of neural networks are one of the simplest variants of neural networks as They pass information in one direction, through various input nodes, until it makes it to the output node. This type of neural network may or may not have hidden node layers, making their functioning more interpretable. Some of the advantages of Artificial Neural Network (or ANN) include 1. they store information on the entire network 2. they have the ability to work with incomplete knowledge 3. they offer fault tolerance and have distributed memory 4. they offer us the ability to work with incomplete knowledge Likewise, the disadvantages include, 1. they have huge hardware dependency 2. they sometimes have unexplained behavior which can leave us tormeneted with results 3. there is no specific rule for determining the structure of artificial neural networks and appropriate network structure is achieved through experience and trial and error Next one on the list, 2. Convolutional Neural Network (CNN) Now, they are one of the most popular models used today. This type of neural network computational model uses a variation of multilayer perceptrons and contains one or more convolutional layers that can be either entirely connected or pooled. Further, these convolutional layers create feature maps that record a region of the image which is ultimately broken into rectangles and sent out for nonlinear processing. Some of the advantages of convolutional Neural Network (or CNN) include 1. They offer very high accuracy in image recognition problems 2. They are capable of automatically detecting important features without any human supervision 3. Weight sharing Likewise, the disadvantages include: 1. CNNs do not encode the position and orientation of object 2. They lack the ability to be spatially invariant to the input data 3. A lot of training data is required in order for it to work efficiently. Next one on the list, 3. Recurrent Neural Network (or RNNs) Now, these are a lot more complex than the ones we discussed so far. They save the output of processing nodes and feed the result back into the model (and hence they did not pass the information in one direction only). This is how the model is said to learn to predict the outcome of a layer. Here, each node in the RNN model acts as a memory cell, continuing the computation and implementation of operations and if the network’s prediction is incorrect, then the system self-learns and continues working towards the correct prediction during backpropagation. Some of the advantages of Recurrent Neural Network (or RNNs) include 1. An RNN remembers each and every information through time. It is useful in time series prediction only because of the feature to remember previous inputs as well, this is called Long Short Term Memory 2. A Recurrent neural network are even used with convolutional layers to extend the effective pixel neighborhood Likewise, some disadvantages include: 1. They have gradient vanishing and exploding problems 2. Training an RNN is a very difficult task 3. It cannot process very long sequences if using tanh or relu as an activation function. Next, let's summarize the difference between ann cnn and rnn in a tabular format, 1. ANNs operate on tabular or text data, whereas CNNs operate on image data and RNNs operate on sequence data 2. Parameter sharing is not possible in ANNs, whereas it is possible in CNNs and RNNs 3. ANNs and CNNs operate on fixed length input, whereas RNNs don't 4. recurrent connections are not possible in ANNs and CNNs whereas in RNNs, they can be achieved 5. Spatial relationships are only possible in CNNs, and not in ANNs and RNNs 6. ANN is considered to be less powerful than CNN, RNN whereas CNN is considered to be more powerful than ANN, RNN and in terms of performance, RNN includes less feature compatibility when compared to CNN 7. The main advantages of ANNs include Having fault tolerance, and the ability to work with incomplete knowledge whereas in CNNs, there is High accuracy in image recognition problems, and it offers Weight sharing further, an RNN remembers each and every information and offers.