Machine learning techniques have been widely applied in various areas such as pattern recognition, natural language processing, and computational learning. During the past decades, machine learning has brought enormous influence on our daily life with examples including efficient web search, self-driving systems, computer vision, and optical character recognition (OCR).
The success of deep neural networks has led to breakthroughs such as reducing word error rates in speech recognition by 30% over traditional approaches (the biggest gain in 20 years) or drastically cutting the error rate in an image recognition competition since 2011 (from 26% to 3.5% while humans achieve 5%).
3 Types of Deep Neural Networks
Three following types of deep neural networks are popularly used today:
- Multi-Layer Perceptrons (MLP)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
Multilayer Perceptrons (MLPs): A MLP is a class of a feedforward artificial neural network (ANN). MLPs models are the most basic deep neural network, which is composed of a series of fully connected layers. Today, MLP machine learning methods can be used to overcome the requirement of high computing power required by modern deep learning architectures. Each new layer is a set of nonlinear functions of a weighted sum of all outputs (fully connected) from the prior one.
CNNs and RNNs are two fundamental types of neural networks widely used in deep learning. While both are powerful tools for processing and analyzing data, they have distinct architectures and are suited for different types of tasks.
CNN vs RNN
The main difference between a CNN and an RNN is the ability to process temporal information or data that comes in sequences, such as a sentence, for example.
CNNs employ filters within convolutional layers to transform data. Whereas RNNs reuse activation functions from other data points in the sequence to generate the next output in a series.
Once we explore the structure of both types of neural networks and understand what they are used for, the differences between a CNN and an RNN will become clear.
What is CNN?
CNNs or convolutional neural networks are a category of neural networks that are majorly used for image classification and recognition. CNNs have been proven to be successful in identifying objects, signs, and even faces. These deep learning algorithms (an important sub-field of data science) take an image as the input to detect and assign importance to the various features of the image in order to differentiate one image from the other.
While simple neural networks have some success in classifying basic binary images, they can’t handle complex images with pixel dependencies. They also don’t have the computational power which is needed to handle images with large pixels, which is exactly where CNNs come in. CNN helps in classifying even the most complex of images with high accuracy. CNN algorithms can also apply relevant filters to identify spatial as well as temporal dependencies in images. Some of the many applications of CNN are;
- Facial recognition
- Analysing documents
- Understanding climate patterns
- Video classification
What is RNN?
RNN or recurrent neural network is a class of artificial neural networks that processes information sequences like temperatures, daily stock prices, and sentences. These algorithms are designed to take a series of inputs without any predetermined size limit. More importantly, what makes RNN unique is that these algorithms process sequences by retaining the memory of the previous value or state in the sequence. So, in RNNs the output of the current step becomes the input of the next step and so on. This means, at every stage, the model considers both the current input and all of the previous outputs. The many applications of RNNs include;
- Speech recognition
- Time series prediction
- Music composition
- Machine translation
Which one is Better?
Type of input data: While RNNs are suitable for handling temporal or sequential data, CNNs are suitable for handling spatial data (images). Though both models work a bit similarly by introducing sparsity and reusing the same neurons and weights over time (in case of RNN) or over different parts of the image (in case of CNN).
Computing power: Since both RNN and CNN are used for different purposes by the data scientists and deep learning researchers, it might not be appropriate to compare their computational ability. Though if we had to, CNN would be more powerful than RNN. That’s mainly because RNN has less feature compatibility and it has the ability to take arbitrary output/input lengths which can affect the total computational time and efficiency. On the other hand, CNN takes fixed input and gives a fixed output which allows it to compute the results at a faster pace.
Architecture: CNNs use the connectivity patterns available in neurons. Inspired by the visual cortex of the brain, CNNs have numerous layers and each one is responsible for detecting a specific set of features in the image. The combined output of all the layers helps CNNs identify and classify images.
RNNs use time-series information to identify patterns between the input and output. The memory of RNN algorithms allows them to learn more about long-term dependencies in data and understand the whole context of the sequence while making the next prediction.