With Machine Learning on the rise, neural network architectures are created to address multiple tasks and problems. Moreover, it consists of artificial neurons that develop complex structures to transform various inputs into one output.
In this article, we will learn about the various neural network architectures in machine learning. However, in order to understand the various neural network architectures, we must first learn the concept of neural networks and their three important categories.
Understanding 7 Neural Network Architectures for Machine Learning
Neural Networks are an essential part of the Deep Learning structure. Further, it mimics the behavior of a human brain and allows computer programs to identify patterns.
Moreover, Neural Networks solve problems in AI, machine learning, and deep learning. As a result, it is also referred to as Artificial Neural Networks.
In addition, it comprises node or neuron layers that contain an input layer, a hidden layer, and an output layer. In fact, each node or neuron layer connects to the other and has an associated weight or threshold.
Most Importantly, if the output of even a single node is different from the specific value, the node activates and sends the data ahead.
Furthermore, neural networks depend on the training data to learn and improve accuracy. Hence, once the algorithms adapt to the accuracy, they become powerful tools.
As a result, the trained algorithms allow the classification of cluster data at a rapid speed. Therefore, tasks that took longer with human interventions can be solved within minutes.
- Feedforward Networks: It refers to the process signals that travel in a single direction that is towards the output layer. Specifically, it consists of an input layer and one output layer with none to multiple hidden layers. Therefore, it is mainly utilized for pattern recognition.
- Feedback Networks: It consists of recurrent or interactive networks. It uses an internal state memory to process the flow of inputs.
As a result, it allows signals to travel in both directions through hidden layers. Hence, it is specifically solves time-series or sequential tasks.
Artificial Neural Networks:
An Artificial Neural Network refers to a group of multiple perceptrons or neurons at every layer. Moreover, it is also known as a Feed-Forward Neural Network as the inputs are processed only in the forward direction.
Furthermore, it consists of three layers, an Input layer to accept inputs, a Hidden layer to process the inputs and an output layer to produce results. Consequently, each layer learns certain weights.
An ANN can solve:
- Tabular Data
- Image Data
- Text Data
Convolutional Neural Networks:
Convolutional Neural Networks are similar to Feedforward networks. Various applications and domains use CNN, especially in image and video projects.
Moreover, CNNs include filters known as kernels. In addition, these kernels extract essential characteristics from the input.
A CNN can help with:
- Image/Video Recognition
- Pattern Recognition
- Computer Vision
Recurrent Neural Networks:
Recurrent Neural Networks are recognized by their feedback loops. Moreover, the time-series data influences these learning algorithms. Especially, for making predictions about future results for stock market predictions or sales forecasting.
Furthermore, its looping constraint allows it to capture sequential information in the input data.
An RNN can help solve:
- Time Series Data
- Text Data
- Audio Data
Although many neural networks consist of the basic three layers, there can be a lot of complex interactions while creating a system. As a result, decisions have to be made regarding the number of nodes, layers, and the nature of algorithms for processing the data.
Moreover, there are at least a thousand options and combinations for neural network architectures. Hence, here are a few criteria to follow while designing neural network architectures.
Keep it Simple:
For an effective and efficient problem-solving neural network structure, it is important to keep the architecture simple. Initially, do not assume to build a structure with a high level of complexity.
Although, begin simple and include complex equations depending on the outcome. Moreover, this would also help design future models for solving various problems.
Build, Train and Test:
It is essential to build, train and test whether the structure is strong rather than accurate. In fact, the system can learn accuracy in the process but needs to maintain a certain strength to address multiple problems.
Avoid Over Training:
Often, overtraining the network leads to an overworked network. Moreover, it is important to build network capable architectures for making decisions for ambiguous data. Above all, it should at least make decisions for more than just the training data.
Track the Results and Outcomes:
It is indeed pivotal to keep track of results and outcomes while building neural network architectures. Therefore, it becomes easier to choose a network design according to the features that work well with the problems.
Moreover, it is important to try various neural network architectures, multiple algorithms, nodes and levels of processing. Hence, it helps in choosing the right permutations and combinations more suitable for the problem-solving requirements.
Monitoring Results while Production:
Above all, it is critical to monitor the results regularly even when the accuracy is consistent. As a result, standards and actual metrics help understand the health of the neural network architectures.
In addition, neural network architectures determine the success of the way algorithms work.
LeNet5 is one of the popular neural architectures developed by Yann LeCun in 1994. Moreover, it is considered to be the first convolutional neural network.
It also has a very basic architecture. Hence, it provides savings on computation and parameters.
Dan Ciresan Net began the implementation of GPU Neural network architectures. In 2010, Jurgen Schmidhuber and Dan Claudiu Ciresan published the 9 layers of neural network architecture. Afterward, it was implemented on the NVIDIA GTX 280 graphics processor with both backward and forward processing.
AlexNet was released by Alex Krizhevsky in 2012. Moreover, it was a much deeper and wider version of LeNet.
Further, it scaled the insights of LeNet to a larger neural network. Hence, making it easier to learn complex algorithms and problems.
Overfeat is the latest derivative of AlexNet. Moreover, it was developed by NYU Lab from Yann LeCun in December 2013.
Further, the article published stated learning bounding boxes, allowing more papers on the topic. Segment objects can be found without learning artificial bounding boxes.
Initially, the first VGG neural network architectures were smaller using 3×3 filters. Further, these 3×3 filters were combined in each convolutional layer as a sequence.
Moreover, the VGG neural network architectures use multiple 3×3 filters to address complex features.
Network-in-Network or NiN is one of the more popular neural network architectures. It offers high combinational power and provides simple yet great insights.
Moreover, it provides a strong combination of the features of the layer while using 1×1 convolutions.
In order to reduce the computational burden on deep neural networks, Christian Szegedy from Google developed GoogLeNet, the first Inception Architecture.
Moreover, it used a stem without inception modules as the initial layer. It also used an average pooling plus softmax classifier similar to NiN.
Above all, the classifier functions with a low number of operations compared to AlexNet and VGG. But, it has also contributed to increasing the efficiency of network designs.
In conclusion, neural network architectures have not just transformed machines, but have also helped scientists understand the human brain better. It is now widely used for financial services, forecasting, market research, risk assessment, etc.