AI Algorithms, The word “Algorithm” has gained prominence and popularity in recent years. It has gradually travelled its path through Mathematical applications to digital marketing and gaming etc.
Now, if we are to categorize “Algorithm”, Machine Learning and Artificial Intelligence get a special mention. If we are to talk about the aspect of AI Algorithm explicitly, it is to be mentioned that the phenomenon is primarily a field of science that deals with, understands and builds intelligent systems.
And in today’s world of digitized paper help, online food deliveries and other expansions; there’s no denying the fact that the AI algorithm, as a digital tool, is here to stay.
In order to evaluate and analyze the performance of the AI algorithms, we need to dissect a few of its aspects, including the different types of AIs, before concluding.
Here’s everything you need to know before going through different AI Algorithms
What is a discriminator?
The discriminator is the CNN that is trained to identify images. It evaluates the output of the generator and sends across signals to them regarding improvement requirements.
What is a generator?
The generator is an inverse network that deals with the functionality which includes a random seed and utilizes the same to generate images.
There’s more to it.
- The generator takes in random series of numbers and returns an image.
- The generator image is fed into the discriminator, alongside with a set of images extracted from the actual, real-time dataset.
- The discriminator accepts both real and fake images and returns probabilities, which is a number 0 and 1, with the numeric 1 representing a prediction of originality, and 0 representing fake.
- The discriminator is in a feedback loop, along with the ground truth dataset of the images.
- The generator remains in a feedback loop with the discriminator.
Let’s check different AI Algorithms
Now that you are aware of the various aspects and features of the AI Algorithm techniques that determine the performances of each one of them, here’s an explicit chart displaying the comparisons for a broader overview.
- Convolutional neural network
- Recurrent neural networks
- Reinforcement learning
- Generative adversarial network
Performance Analysis of Convolutional Neural Networks (CNNs)
This is one technique that comprises a hierarchical structure where convolution filters sample an image into a lower resolution map. It is said that the map represents the value of the convolution operation at each point.
Determining maximum performance:
- The algorithm technique, while used in images goes from high-resolution pixels to exceptional features (edges and circles, to coarse features (lips, eyes, and nose), and finally to the fully connected layer that can identify what is there in the image.
- The best part of Convolutional Neural Networks is that the convolution filters are randomly initialized. This, as a result, ensures the fact that when you are training the network, you are preparing the convolution filters.
- CNN’s are trained by feeding images into the network, along with the use of back-propagation to supply the error between the correct label for representation and the result.
- The filter kernels are finely adjusted by a gradient descent technique.
Performance Analysis of Recurrent Neural Networks
This is yet another AI Algorithm technique that works well for time series or sequential data. The neural node in the Recurrent Neural Networks is considered as a kind of memory gate, termed as Long Short Term Memory Cell or LSTMC. When these are linked up in layers of a neural net, these nodes/cells also comprises recurrent connections that loop back into themselves.
As a consequence, they tend to retain the information that passes through them in the form of programming help. Thus, the network retains memory and allows the processing of the past and current information on the network.
Features to determine how RNN works:
The Recurrent Neural Networks are suitable for time-sequential operations, including language translation and processing. RNN is equally applicable for operations that include text to speech and speech to text translations.
It is to be noted that Recurrent Neural Networks are typically trained by back-propagation and a pinch of gradient descent.
Performance Analysis of Reinforcement Learning
Assessing the performance of the various AI algorithm techniques, Reinforcement Learning gets a special mention. It allows you to train a learning agent to solve a complex problem. Here’s get a case study help that works.
- It takes the best action in a given state, with the probability of choosing each step at each of the rules, defined by the policy.
- Reinforcement learning is inspired by behaviourist psychology.
Example
Let’s take the example of running a maze. In this situation, the position of each cell is the ‘state’, the four possible directions to move are the actions and the probability of moving each of the directions, at each cell or state forms the policy.
By repeatedly running through the cells and possible actions, and rewarding the sequence of steps that came up with a good result (by increasing the probabilities of those actions in the policy), and penalizing the responses that came up with a negative effect (by reducing the chances of those actions in the plan), you reach at an optimal policy.
This has the maximum probability of a successful result. Usually, while training, you reduce the penalties/rewards for further actions shortly.
Performance Analysis of Generative Adversarial Networks
The Generative Adversarial Network is currently used with Convolutional Neural Network (CNN). It is said to be more of a technique than architecture. It is mostly used to create image discriminators and generators. Before delving deeper, let’s explore the technical meanings of generators and discriminators.
The technique, when used in images, goes from high-resolution images to fine features, coarse features and finally to fully connected layers.
The RNN algorithm is primarily used to ensure sequential operations, including language translation and processing.
The technique associated with reinforcement learning is inspired by behavioural psychology. The technique is used with Convolutional Neural Network, mostly used to create image discriminators and generators.
The convolution filters on CNN are randomly initialized. There are no filter kernels associated with this technique. Reinforcement Learning is inspired by behaviourist psychology instead of kernel filter
The Generative Adversarial Network is primarily based on the functionalities of discriminators and generators. The network is trained by feeding images into it.
The Recurrent Neural Networks is typically trained by back-propagation and gradient descent. Reinforcement Learning allows you to train an agent to solve complex problems. The discriminator in the Generative Adversarial Network is trained to identify images.
I hope the blog serves its purpose of building insights regarding the performance of various AI Algorithms. Invest some time in brainstorming, recapitulate the evaluations thoroughly, and embrace the best technique that would suit your industry niche in the longer run.
