Machine Learning vs. Neural Networks

Introduction

You know that artificial intelligence has made major developments when you see AI movies like I Robot, Terminator, and many such sci-fi movies slowly coming to life. It is slowly but surely finding its way into every field, and eventually into our daily life too. Two major fields of AI that have been thrown around the most are Machine Learning and Neural Networks. Both of these concepts are relevant to the field of AI and work towards reducing manual labor with as much accuracy as possible.

The key difference between machine learning and neural networks lies in their fundamental concept and origins. Machine learning models are developed through algorithms that provide the model to train and update itself through processing the given data. They do not require explicit programming whereas neural network models are those that use complex algorithms that can mimic the operations of a human brain closely.

Difference Between Machine Learning and Neural Networks in Tabular Form

Parameters of Comparison	Machine Learning	Neural Networks
Definition	It works through a group of algorithms that receive data and process the parsed data to help improve the logic and learn from it.	It is a network built on nodes that imitate neurons in a human brain and simulate similar complex functions.
Number of layers/levels	Machine learning only has an input or output data layer to the model.	Neural networks have several layers that help build the communication system.
Decision making	A machine learning model makes decisions by themselves based on the data parsed.	Neural network models make decisions based on the complex algorithms fed to the model.
Categories	Machine learning can be further divided into Supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.	Neural networks have seven different categories and some of them are Perceptron, convolutional, recurrent, feed-forward, etc.,
Skills required	Big data, statistics, DS probability, Hadoop, etc.	Mathematics, probability, statistics, DS, etc.
Fields	E-commerce, Customer service, delivery systems, etc.	Finance, Healthcare, Stock exchange prediction, etc.
Examples	Siri, Google maps, Text predictions, etc.	Search engines, image recognition, etc.

What is Machine Learning?

Machine learning falls under the category of artificial intelligence that focuses on training computer models to acquire and interpret data with the least amount of human intervention. These models then process whatever data is acquired and update themselves accordingly. These updates allow the model to make more accurate decisions, thus reducing any extra human guidance to better the model. Hence, to build such models, one requires both the foundations of statistics and computer science.

However, it is important to note that machine learning is not synonymous with conventional programming. Conventional programming involves the program and the output being fed to the machine that delivers an output. A machine learning model will process the input and the output to build a unique logic on its own i.e., the program. This logic is entirely based on the data fed and the outcomes of the same. How exactly does this process take place?

The Development of Logic:-

The chief and defining advantage of machine learning is its ability to upgrade itself through several test cases. Let us consider three factors: Task(T), Experience(E), and Probability(P) that can be used to characterize any basic operation. The tasks help in processing the outcome of a certain program. Through repeated iterations and test cases of a certain task, one can calculate the probability of a certain result. The repetitions help gain experience through the machine and can learn how a certain model behaves. Thus, the established logic keeps developing when a different outcome is achieved.

Types of Machine Learning:-

To build a machine that can learn by itself is not conformed to a single method. There exist different ways of learning that can be adopted by a machine learning model based on the type of data fed into the model.

Supervised learning: This type of learning is adopted by models that involve the concept of a labeled data set. This means the dataset fed to the model has both inputs and desired outputs. The data is usually split in an 80:20 ratio where 80% of the data is used for training the model and the rest 20% is used to test the model. Both the input data and the output data are included in this training data. Supervised learning can be further categorized into classification and regression.

Eg:- Decision trees, Linear regressions, Random forest

Unsupervised Learning: Unlike the formerly described type, Unsupervised learning does not provide any output values but only the input parameters. Thus it does not have any targeted result it must struggle to achieve. Here, we feed both unstructured data that has vague, unknown, or missing data and unlabeled data that has no target output. Unsupervised learning can also be classified further into two different kinds: Clustering and Association

Eg:- Hierarchical clustering, BIRCH- Blanched Iterative Reducing & Clustering using Hierarchies.

Semi-supervised Learning: The techniques of semi-supervised learning lie somewhere in between those of supervised learning and unsupervised learning. This type of learning involves datasets where only a small portion of the data is labeled and the rest of it remains unlabeled.

Eg:- Image sets with semi-labeled data

Reinforcement Learning: This type of learning is particular to the problem posed, owing to the fact that the model upgrades its efficiency through feedback mechanisms. Every time a new piece of data is fed, the model updates its model by adjusting its training data better. This means that the model performs better each time it is trained.

Eg:- Q-learning, Deep adversarial networks, Temporal Difference (TD)

What are Neural Networks?

A neural network can simply be explained as a group of neurons that establish a network between each of these units called nodes to feed information forward. When speaking in terms of Machine learning and other such computing contexts, Neural networks, more accurately known as Artificial Neural Networks(ANN), are an example of a machine learning model, more specifically an unsupervised learning model, that mimics the functions of a human brain and simulates them through various algorithms. This is but another step towards the realization of various artificial intelligence theories, making it the entire foundation of the AI domain. To understand the concept more clearly, let us look at how neural networks came to life.

Origins and Inspiration:-

The first chapter of the story of how neural networks came to life began in 1943 when a neurophysicist named Warren McCulloch and a mathematician named Walter Pitts came together to write a paper about the possible working of neurons. The world always aspires to mimic natural phenomena, both bodily and environmental, to automate and reduce manual labor as much as possible. In this pursuit of mass automation, the most challenging chase was to mimic the operations of the most complex object known to humankind: the human brain.

The human brain is connected to the nervous system, which we all know is the communication network that works across the human body. To dive further, it is made up of neurons, each of which is the most basic computational unit of a human brain. The nerve endings also known as Synapses can be described as the mediators that link these neurons and help them interact with each other. However, a single neuron connection is not what a human brain needs to handle the body. There are approximately a mind-boggling number of 10 billion neurons along with about 60 trillion synapses. It is also important to note that these networks do not operate in a single layer but in multiple layers, some of which are hidden. Now how exactly is this neural system integrated into the human body?

When the human body receives an external stimulus, the receptors receive this stimulus and feed the information forward through electrical signals. These signals are processed and forwarded through the neural network explained above and the reaction to these stimuli is fed through to the effectors that display the same.

The Analogy and Development:-

To model an artificial neural network in at least a similar way to how a human neural network works, we can begin by considering the human brain as a highly complex, information processor that can parallelly run multiple operations. Each neuron can be considered a node in the network that carries forward any signal received to its neighboring nodes. Thus, groups of such nodes are accumulated together to make up an ANN. A basic ANN can be considered a machine that is programmed to run a certain way or produce a certain desired result, similar to how a brain functions. A complex neural network can have different levels that operate the same basic functions parallely. However, each of these neural networks needs to have certain qualities to be considered capable-

Adaptivity
Nonlinearity
Fault-tolerant
contextual/ relevant information
Appropriate response
Implementability in VLSI
I/P & O/P mapping
Design and logic uniformity

Types of Neural Networks:-

Categorized based on the number of levels and the operations of each, there are about seven different types of neural networks.

Perceptron: It does not contain any hidden layers and operates on basic tasks such as classification. It is one of the oldest forms of neural networks.
Multi-layer perceptron: Also known as dense networks, Multilayer perceptrons work in a bidirectional manner, incorporating multiple hidden layers. They can work with both forward and backward propagation.
Feed-forward: These networks work only in a fee-forward manner and do not support backward propagation. They too consist of multiple hidden layers and are used for various recognition functions such as speech recognition, facial recognition, etc.
Radial-based networks: They work in three layers with the main layer consisting of a Radial Basis Function(RBF) that stores classes for training the data instances provided.
Convolutional neural networks(CNN): CNN's use custom matrices, also called filters, that help in convolution and image classification.
Recurrent neural networks(RNN): RNNs are used when sequential data such as sequences of images or words need predictions.
Short-term memory networks: Also known as Long Short-Term Memory Networks(LSTM) help store information for long periods that involve a special memory cell. These are used for applications such as speech recognition and text prediction.

Main Differences Between Machine Learning and Neural Networks In Points

Machine learning models are based on algorithms that help the model process the given input and output to upgrade its logic. This helps the model stay as accurate as possible. Neural networks, on the other hand, are developed through algorithms that can help the model mimic complex functions that a human brain can perform.
Machine learning models do not have multiple paths that need to work simultaneously. It has a single layer that describes the input or the output feed of the data. In contrast to this, neural networks require several hidden layers to work and establish a network that can function properly.
Both these models are developed to make decisions. Machine learning models are developed to make decisions based on the data processed whereas neural network models make decisions through the complex algorithms integrated into the model.
Machine learning can be categorized into four different types based on the datasets provided to the model and its mechanism. They are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Neural networks are categorized into seven different categories: Perceptron, multilayer perceptron, feed-forward, convolution, recurrent, short-term memory, and radial-based.
The skills required to make a proper machine learning model is the expertise in Big data, Statistics, DS, probability, and Hadoop whereas the skills require expertise in mathematics, probability, DS, and statistics.
Machine learning is used in major fields such as E-commerce, betterment of customer service, delivery systems, and many more. Neural systems on the other hand are used in finance, healthcare, stock exchange prediction, etc.
Some examples of such applications of machine learning are Siri, google maps, text predictions, etc. Neural networks, on the other hand, are used for search engines and image recognition.

Conclusion

Though both these concepts have a wide syllabus and the potential to develop AI as much as possible, they differ greatly in their fundamental concepts. Machine learning has a high potential to reach the goal of mass automation and reduction of human intervention but neural networks work towards mimicking the complexity of a human brain. Thus, they are two very different concepts and help greatly in the field of robotics.

References

Introduction
Machine Learning vs. Neural Networks
Difference Between Machine Learning and Neural Networks in Tabular Form
What is Machine Learning?
What are Neural Networks?
Main Differences Between Machine Learning and Neural Networks In Points
Conclusion
References

Difference Between Machine Learning and Neural Networks

Introduction