One step forward in Machine Learning.
Artificial Intelligence is the term getting overwhelming in the market nowadays. It occupied a major sector of Information Technology and still occupying more. The reason for this is that it is making tedious and time-consuming work easier with fewer amount of resources. Artificial Intelligence consists of many parts but we are going to focus on Machine learning mainly Deep Learning.
Machine Learning is the trend that makes the machine capable of making predictions based on and studying the history of that scenario. Like, suppose we want to do weather forecasting of some particular region. Now we have to train our model which means our machine with previous data of weather reports of that region in various seasons and then our model gets to know about how to do weather forecasting. But Machine Learning has some limitations as it can work on only numerical data and the performance of these Machine Learning models on complex data is not that satisfactory. So one step forward to Machine Learning, one new term introduced called Deep Learning.
Our human brain consists of many complex neuron structures which are interconnected to each other and pass information from nodes to nodes. Node means the thing which takes many inputs and gives only one output. Based on this fact, Deep Learning works.
Deep learning is the subset of machine learning which is able to find the trend in complex datasets with great power and flexibility. It consists of mainly three parts
1: Artificial Neural Networks (ANN)
2: Convolutional Neural Networks (CNN)
3: Recurrent Neural Networks (RNN)
Artificial Neural Network consists of neural network structures called Dense network in a sequential manner that means one set of neurons followed by another one. This Neural Network is used to do regression and classification tasks with numerical data.
Convolutional Neural Network is basically used to work on image data classification. It consists of Convolution in different dimensions with different filters. These filters are used to find verticle, horizontal edges in the images, So that model get to know the relation in the dataset.
Recurrent Neural Network used mainly in Natural Language Processing with a different set of Dense networks.
Not only these but there are also other very useful things are in deep learning like state of art models, transfer learning, object detection etc. Scientists and researchers are still working on the new trend in Deep Learning. In 2050, No one would believe this much that technology will move forward this much.
There are many interesting things in Deep Learning to elaborate but I think this would be enough for just the introductory part. If there is an error in my explanation please let me know, Thank you for reading my article 🙂