Artificial Neural Networks (ANNs), commonly referred to as Neural Networks (NNs), are computing systems inspired by the biological neural networks that make up the human brain.
Neural Networks: A Brief History
Neural networks sound like exciting new technology, but the field itself is nothing new. In 1958, American psychologist Frank Rosenblatt conceived and attempted to build a machine that reacts like a human mind. He named his machine the "perceptron".
For all practical purposes, artificial neural networks learn
by example in a human-like manner. External input is received, processed and
manipulated in the same way as the human brain.
Hierarchical Structure of Neural Networks
We know that different parts of the human brain are used to
process various kinds of information. These parts of the brain are arranged in
layers. As information enters the brain, each layer or level of neurons does
its special job of processing incoming information, gaining insights, and
passing them on to the next higher-level layer. For example, when you walk
through a bakery, your brain responds to the scent of freshly baked bread in
stages:
- Data Input: The Taste of Freshly Baked Bread
- Thought: This reminds me of my childhood
- Decision: I think I'll buy some bread
- Memories: But I already had lunch
- Reasoning: Maybe I can have a snack
- Action: I'm going to buy one of that bread
This is how the brain works in stages. Artificial neural networks work in a similar way. Neural networks try to emulate this multi-layered approach to processing various information inputs and making decisions based on them.
At the cellular or individual neuron level, function is
fine-tuned. Neurons are nerve cells in the brain. Nerve cells have tiny
extensions called dendrites. They receive the signal and transmit it to the
cell body. The cell body processes stimuli and decides to trigger signals from
other neurons in the network. If a cell decides to do so, extensions on the
cell body called axons will transmit signals to other cells through chemical
transmission. The operation of neural networks is inspired by the function of
neurons in our brains, although their technical mechanisms of action differ
from biological ones.
How neural networks work in a way similar to the human brain
The most basic form of artificial neural network has three layers of neurons. Information flows from one layer of neurons to another, just like in the human brain:
- Input layer: the entry point for data into the system
- Hidden layer: where the information is processed
- Output layer: where the system decides how to proceed based on the data
More complex artificial neural networks have multiple layers, some of which are hidden.
Neural networks operate through collections of nodes or connected units, just like artificial neurons. These nodes loosely model the network of neurons in the animal brain. Just like humans, artificial neurons receive signals in the form of stimuli, process them, and send signals to other neurons connected to them.
The similarities end there.
Neuron Operation of Artificial Neural Network
In artificial neural networks, artificial neurons receive stimulation in the form of real signals. Then:
- The output of each neuron is computed as a nonlinear function of the sum of its inputs.
- The connections between neurons are called edges.
- Both neurons and edges have weights. This parameter adjusts and changes as learning progresses.
- Weights increase or decrease the strength of the signal at the connection.
- Neurons may have a threshold. Signals are sent forward only if the aggregated signal exceeds this threshold.
As mentioned earlier, neurons are aggregated into layers. Different layers may modify their inputs differently. The signal moves from the first layer (input layer) to the last layer (output layer) in the manner discussed above, sometimes traversing between layers multiple times.
Neural networks essentially contain some form of learning rules that modify the weights of neural connections based on the input patterns presented by them, much like a growing child learns to recognize animals from their examples.
Neural Networks and Deep Learning
When it comes to neural networks, it is impossible not to
mention deep learning. Although the terms "neural network" and
"deep learning" are different from each other, they are often used
interchangeably. However, the two are closely related as one relies on the
other to function. If neural networks don't exist, deep learning doesn't exist
either:
- Deep learning has come to the fore of physical AI that is already at the forefront.
- Deep learning is different from machine learning, which aims to teach computers to process and learn from data.
- With deep learning, computers can continually train themselves to process data, learn from it, and build more capabilities. Multiple layers of more complex artificial neural networks make this possible.
- Complex neural networks contain input and output layers, just like simple forms of neural networks, but they also contain multiple hidden layers. Hence, they are called deep neural networks and are good for deep learning.
- Deep learning systems teach themselves and become more "knowledgeable" as they evolve, filtering information through multiple hidden layers in a way similar to all the complexities of the human brain.
Why Deep Learning Matters to Organizations
Deep learning is like the new gold rush or the latest oil discovery in tech. The potential of deep learning has piqued the interest of large established enterprises as well as emerging startups and various other companies. Why?
This is part of a larger data-driven environment, especially due to the increased importance of big data. If you think of internet-derived data as crude oil stored in databases, data warehouses, and data lakes waiting to be drilled with various data analysis tools, then deep learning is the refinery that transforms crude oil data into a final product that you can use product.
The market is flooded with data-driven analytics tools, and deep learning is the end of that data: without efficient and state-of-the-art processing units, extracting anything of value is useless.
Deep learning has the potential to replace humans by
automating repetitive tasks. However, deep learning cannot replace the thought
process of a human scientist or engineer creating and maintaining deep learning
applications.
Distinguish machine learning from other types of learning
Machine Learning
When it comes to machine learning methods, the key lies in training learning algorithms such as linear regression, K-means, decision trees, random forests, k-nearest neighbors (KNN) algorithms, and support vector machine (SVM) algorithms.
These algorithms sift through datasets, learning as they evolve to adapt to new situations and look for interesting and insightful data patterns. Data is the key foundation on which these algorithms work best.
Supervised Learning
Datasets used to train machine learning can be annotated. The dataset comes with answers that tell the computer the correct answer. For example, a computer scanning an inbox for spam can refer to a labeled dataset to see which emails are spam and which are legitimate. This is called supervised learning. Supervised regression or classification is achieved through linear regression and K-Nearest Neighbors algorithm.
Unsupervised Learning
When the dataset is not labeled and directed to aggregate clustering patterns without any reference table using an algorithm such as K-means, it is called unsupervised learning.
Neural Networks and Fuzzy Logic
By the way, it is also important to distinguish between neural networks and fuzzy logic. Fuzzy logic allows specific decisions to be made based on imprecise or ambiguous data. Neural networks, on the other hand, attempt to solve problems by incorporating human-like thought processes without first designing a mathematical model.
How are neural networks different from traditional computing?
To better understand how computation works with artificial neural networks, one must understand traditional "serial" computers and their software process information.
Serial computers have a central processing unit that can address an array of memory locations where data and instructions are stored. The processor reads the instruction and any data required by the instruction from the memory address. The instruction is then executed and the result is saved in the specified memory location.
In serial or standard parallel systems, computational steps are deterministic, performed sequentially, and logically. Additionally, the state of a given variable can be tracked from one operation to another.
How Neural Networks Work
In contrast, artificial neural networks are neither sequential nor necessarily deterministic. They do not contain any complex central processing unit. Instead, they consist of a few simple processors that take a weighted sum of their inputs from other processors.
Neural networks do not execute programming instructions. They respond in parallel (either simulated or real) to the input patterns presented to it.
Neural networks do not contain any separate memory addresses for data storage. Instead, the information is contained in the overall activation state of the network. Knowledge is represented by the network itself, which is in fact far more than the sum of its components.
Advantages of Neural Networks over Traditional Techniques
Neural networks can be expected to train themselves very efficiently if there is a problem where the relationship is dynamic or non-linear. This capability is further enhanced if the internal data schema is strong. It also depends to some extent on the application itself.
Neural networks are analytical alternatives to standard techniques and are somewhat limited to concepts such as linearity, normality, and strict assumptions about the independence of variables.
With the ability of neural networks to examine various relationships, users can more easily and quickly model phenomena that may be difficult or even impossible to understand.
Limitations of Neural Networks
Potential users should be aware of some specific issues, especially those related to backpropagation neural networks and certain other types of networks.
Process cannot be explained
Backpropagation neural networks are known as the ultimate black box. Aside from outlining the general architecture and possibly using some random number as a seed, all the user needs to do is provide input, continue training, and then receive output. Some software packages allow users to sample the progress of the network over time. In these cases, the learning itself progresses on its own.
The final output is a trained autonomous network because it does not provide equations or coefficients that define mathematical relationships beyond its own internals. The network itself is the ultimate equation for this relationship.
Slower to Train
Also, backpropagation networks tend to be slower to train than other types of networks, sometimes requiring thousands of epochs. This is because the machine's central processing unit has to calculate the capabilities of each node and connection separately. This can be very cumbersome and can cause problems in very large networks with large amounts of data. However, contemporary machines do work fast enough to circumvent this problem.
Applications of Neural Networks
Neural networks are general approximations. They happen to work best if the system has a high tolerance for errors.
Uses of Neural Networks:
- Used to understand associations or discover common elements in a set of patterns
- Both the number and the variety of parameters, the data is very large
- Relationships between variables are vaguely understood
- Where traditional methods are insufficient in describing relationships
- This beautiful biologically inspired paradigm is one of the most subtle technological developments of our time.