Top Five Deep Learning Techniques

Top Five Deep Learning Techniques

Introduction

Artificial Intelligence (AI), data science,  and Machine Learning (ML) have changed the world around us with their innovations. Moreover, it is the many deep learning techniques that take ML to a whole new level where machines can learn different tasks. These models are being aggressively used for cancer detection and clinical treatment in the medical field.

Top Five Deep Learning Techniques

There are different kinds of deep learning models that effectively tackle problems that are difficult for the human brain. Moreover, these predictive analysis models are accurate too. Let us go through some of them.

1. Classic Neural Networks

Classic Neural Networks or Fully Connected Neural Networks is often recognized by its multilayer perceptrons, where the neurons are linked to the continuous layer. Designed by Fran Rosenblatt, in 1958, this technique comprises the adaptation of the model into fundamental binary data inputs.

This Deep Learning Techniques Works Best In

1. Any table dataset which has rows and columns formatted in CSV.
2. Regression and classification issues with the input of real values.
3. Any model with the highest flexibility, like that of ANNS.

 

2. Convolutional Neural Networks

CNN is a high-potential and advanced type of the classic artificial neural network model. It is created for solving higher complexity, pre-processing, and data compilation. This technique takes reference from the arrangement order of neurons available in the visual cortex of an animal brain.

This Deep Learning Techniques Works Best In

The CNNs are adequate for tasks comprising image analyzing, image recognition, video analysis, image segmentation, and natural language processing.

3. Recurrent Neural Networks (RNNs)

The RNNs were initially designed to help forecast sequences, such as the Long Short-Term Memory (LSTM) algorithm known for its multiple functionalities. Such networks work on data sequences of the variable input length. The RNN invests the knowledge acquired from its previous state as an input value for the present forecast.

This Deep Learning Techniques Works Best In

1. One to One: A single input connected to a single output, such as image classification.
2. One to many: A single input connected to output sequences, such as image captioning that comprises several words from a single image.
3. Many to One: Series of inputs creating a single output, such as sentiment analysis.
4. Many to many: Series of inputs yielding series of outputs, such as video classification.

 

4. Generative Adversarial Networks

This technique is a combination of two deep learning techniques of neural networks –a Discriminator and a Generator. The Discriminator helps in discriminating between real and false data, while the Generator Network yields artificial data. Both the networks are competitive, as the Generator keeps creating artificial data similar to real data – and the Discriminator endlessly detects real and unreal data.

Works Best In

1. Image Enhancement
2. Image and Text Generation
3. New Drug Discovery processes

5. Self-Organizing Maps

The SOMs or Self-Organizing Maps function with the help of unsupervised data that decreases the number of arbitrary variables in a model. In this technique of deep learning, the output dimension is set as a two-dimensional model, as each synapse links to its output and input nodes.

 Works Best In

1. When the datasets do not come with a Y-axis value.
2. Project explorations for analyzing the dataset framework.
3. Creative projects in videos, text, and music with the help of AI.

Conclusion

There are multiple deep learning techniques available with their respective functionalities and practical approach. Identifying these models and for the appropriate scenarios result in achieving high-end solutions based on the framework used by developers.