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Artificial Intelligence vs. Machine Learning vs. Deep Learning

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Brief Description: In 2020, people benefit from artificial intelligence every day: music recommender systems, Google maps, Uber, and many more applications are powered with AI. However, the confusion between the terms artificial intelligence, machine learning, and deep learning remains.

Let’s clear things up: artificial intelligence (AI), machine learning (ML), and deep learning (DL) are three different things.

Artificial intelligence is a science like mathematics or biology. It studies ways to build intelligent programs and machines that can creatively solve problems, which has always been considered a human prerogative.

Machine learning is a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. In ML, there are different algorithms (e.g. neural networks) that help to solve problems.

There are basically three types of Machine Learning as shown below:

Supervised Learning

In supervised learning, the machine is provided with the labelled dataset. It already has input and output parameters. So, when the machine is given a new dataset, the supervised learning algorithm examines the data and produces the correct output according to the labelled data.

Un-supervised Learning

In unsupervised learning, the machine would not have any labelled dataset. The algorithm is designed in a way that it tries to learn by itself without any supervision of data. This involves clustering of data.

Reinforcement Learning

In reinforcement learning, the algorithms are designed in such a way that the machine tries to find an optimal solution. It adopts the principle of reward and punishment, and by this approach it moves to the correct result.

Deep learning, or deep neural learning is a subset of machine learning, which uses the neural networks to analyse different factors with a structure that is similar to the human neural system.

Deep Learning is basically mimicking the human brain, it can also be defined as a multi neural network architecture containing a large number of parameters and layers. The three fundamental network architectures are as listed below:

Convolutional Neural Networks

Convolutional Neural Network is basically an artificial neural network that is most widely used in the field of Computer Vision for analysing and classifying images. It is a deep learning algorithm that takes the input image and assigns weights/biases to various aspects or objects in the image, so that it can differentiate one from the other. The hidden layers of a CNN typically consist of convolutional layers, pooling layers, fully connected layers, and normalization layers. The architecture of a ConvNet is analogous to that of the connectivity pattern of Neurons in the Human Brain and was inspired by the organization of the Visual Cortex.

Recurrent Neural Networks

Recurrent Neural Networks is a type of neural network architecture that is used in sequence prediction problems and is heavily used in the field of Natural Language Processing. RNNs are called recurrent because they perform the same task for every element of a sequence, with the output being dependent on the previous computations. Another way to think about RNNs is that they have a “memory” which captures information about what has been calculated so far.

Recursive Neural Networks

A recursive neural network is more like a hierarchical network where there is really no time aspect to the input sequence but the input has to be processed hierarchically in a tree fashion. Here is an example of how a recursive neural network looks. It shows the way to learn a parse tree of a sentence by recursively taking the output of the operation performed on a smaller chunk of the text.