Published on Aug 28,2019 2.4K Views
With the advancement in Machine Learning, Artificial Intelligence has taken a high road.
Deep Learning is considered to be the most advanced technology built to solve complex problems that use massive data sets.
This blog on what is a Neural Networks will introduce you to the basic concepts of Neural Networks and how they can solve complex data-driven problems.
To get in-depth knowledge of Artificial Intelligence and Deep Learning, you can enroll for live Deep Learning with TensorFlow Training by Edureka with 24/7 support and lifetime access.
Here’s a list of topics that will be covered in this blog:
Simple Definition Of A Neural Network
Modeled in accordance with the human brain, a Neural Network was built to mimic the functionality of a human brain. The human brain is a neural network made up of multiple neurons, similarly, an Artificial Neural Network (ANN) is made up of multiple perceptrons (explained later).
A neural network consists of three important layers:
- Input Layer: As the name suggests, this layer accepts all the inputs provided by the programmer.
- Hidden Layer: Between the input and the output layer is a set of layers known as Hidden layers. In this layer, computations are performed which result in the output.
- Output Layer: The inputs go through a series of transformations via the hidden layer which finally results in the output that is delivered via this layer.
Before we get into the depths of how a Neural Network functions, let’s understand what Deep Learning is.
What Is Deep Learning?
Deep Learning is an advanced field of Machine Learning that uses the concepts of Neural Networks to solve highly-computational use cases that involve the analysis of multi-dimensional data. It automates the process of feature extraction, making sure that very minimal human intervention is needed.
So what exactly is Deep Learning?
Deep Learning is an advanced sub-field of Machine Learning that uses algorithms inspired by the structure and function of the brain called Artificial Neural Networks.
Difference Between AI, ML, and DL (Artificial Intelligence vs Machine Learning vs Deep Learning)
People often tend to think that Artificial Intelligence, Machine Learning, and Deep Learning are the same since they have common applications. For example, Siri is an application of AI, Machine learning and Deep learning.
So how are these technologies related?
- Artificial Intelligence is the science of getting machines to mimic the behavior of humans.
- Machine learning is a subset of Artificial Intelligence (AI) that focuses on getting machines to make decisions by feeding them data.
- Deep learning is a subset of Machine Learning that uses the concept of neural networks to solve complex problems.
To sum it up AI, Machine Learning and Deep Learning are interconnected fields. Machine Learning and Deep learning aids Artificial Intelligence by providing a set of algorithms and neural networks to solve data-driven problems.
Now that you’re familiar with the basics, let’s understand what led to the need for Deep Learning.
Need For Deep Learning: Limitations Of Traditional Machine Learning Algorithms and Techniques
Machine Learning was a major breakthrough in the technical world, it led to the automation of monotonous and time-consuming tasks, it helped in solving complex problems and making smarter decisions. However, there were a few drawbacks in Machine learning that led to the emergence of Deep Learning.
Here are some limitations of Machine Learning:
- Unable to process high dimensional data: Machine Learning can process only small dimensions of data that contain a small set of variables. If you want to analyze data containing 100s of variables, then Machine Learning cannot be used.
- Feature engineering is manual: Consider a use case where you have 100 predictor variables and you need to narrow down only the significant ones. To do this you have to manually study the relationship between each of the variables and figure out which ones are important in predicting the output. This task is extremely tedious and time-consuming for a developer.
- Not ideal for performing object detection and image processing: Since object detection requires high-dimensional data, Machine Learning cannot be used to process image data sets, it is only ideal for data sets with a restricted number of features.
Before we get into the depths of Neural Networks, let’s consider a real-world use case where Deep Learning is implemented.
Deep Learning Use Case/ Applications
Did you know that PayPal processes over $235 billion in payments from four billion transactions by its more than 170 million customers? It uses this vast amount of data to identify possible fraudulent activities among other reasons.
What is an Artificial Neural Network (ANN)?
In information technology (IT), an artificial neural network (ANN) is a system of hardware and/or software patterned…
after the operation of neurons in the human brain. ANNs — also called, simply, neural networks — are a variety of deep learning technology, which also falls under the umbrella of artificial intelligence, or AI.
Commercial applications of these technologies generally focus on solving complex signal processing or pattern recognition problems. Examples of significant commercial applications since 2000 include handwriting recognition for check processing, speech-to-text transcription, oil-exploration data analysis, weather prediction and facial recognition.
An ANN usually involves a large number of processors operating in parallel and arranged in tiers. The first tier receives the raw input information — analogous to optic nerves in human visual processing.
Each successive tier receives the output from the tier preceding it, rather than from the raw input — in the same way neurons further from the optic nerve receive signals from those closer to it.
The last tier produces the output of the system.
Each processing node has its own small sphere of knowledge, including what it has seen and any rules it was originally programmed with or developed for itself.
The tiers are highly interconnected, which means each node in tier n will be connected to many nodes in tier n-1 — its inputs — and in tier n+1, which provides input data for those nodes.
There may be one or multiple nodes in the output layer, from which the answer it produces can be read.
Artificial neural networks are notable for being adaptive, which means they modify themselves as they learn from initial training and subsequent runs provide more information about the world.
The most basic learning model is centered on weighting the input streams, which is how each node weights the importance of input data from each of its predecessors.
Inputs that contribute to getting right answers are weighted higher.
Typically, an ANN is initially trained or fed large amounts of data. Training consists of providing input and telling the network what the output should be.
For example, to build a network that identifies the faces of actors, the initial training might be a series of pictures, including actors, non-actors, masks, statuary and animal faces.
Each input is accompanied by the matching identification, such as actors' names, “not actor” or “not human” information. Providing the answers allows the model to adjust its internal weightings to learn how to do its job better.
For example, if nodes David, Dianne and Dakota tell node Ernie the current input image is a picture of Brad Pitt, but node Durango says it is Betty White, and the training program confirms it is Pitt, Ernie will decrease the weight it assigns to Durango's input and increase the weight it gives to that of David, Dianne and Dakota.
What is a neural network: An introduction
In this series we work towards deep learning. Deep learning is a complex form of machine learning, which we discussed in previous articles.
Deep learning consists of the exposure of multilayer neural networks to enormous amounts of data. Deep learning is thus made possible, as it were, by neural networks.
Before we discuss deep learning, we therefore first discuss neural networks. In this introduction we give a definition: what is a neural network?
What is a neural network: A definition
Neural networks are an important approach in machine learning. These networks are composed of multiple but simple processors that work in parallel to model (non-linear) systems, where there is a complex relationship between input and output. Analogous to our brains, the processors in a neural network are also called neurons.
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A neural network usually consists of multiple layers: an input layer of neurons representing the input of a problem, an output layer of neurons representing the solution of the problem, and intermediate layers with artificial neurons that perform calculations. Each connection can transfer a signal to another neuron. Neurons have a weight that can increase or decrease the power of the transmitted signal. In other words, neurons are activated via weighed connections of previously active neurons. The receiving neuron processes the signal and then sends a signal to the next neurons. The threshold is important here: Only if the aggregate of the signal is lower (or higher) than that threshold level is the signal transmitted.
What is a neural network: It can learn
The interaction between the processors in a neural network is adaptive, so that connections between other processors in the neural network can be formed, and existing connections can be strengthened, weakened or broken again. This means that a neural network can learn. Here, ‘learning’ refers to the automatic adjustment of the parameters of the system, so that the system can generate the correct output for a given input.
What is a neural network: An example
A Basic Introduction To Neural Networks
The simplest definition of a neural network, more properly referred to as an 'artificial' neural network (ANN), is provided by the inventor of one of the first neurocomputers,
Dr. Robert Hecht-Nielsen. He defines a neural network as:
“…a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.
In “Neural Network Primer: Part I” by Maureen Caudill, AI Expert, Feb. 1989
ANNs are processing devices (algorithms or actual hardware) that are loosely modeled after the neuronal structure of the mamalian cerebral cortex but on much smaller scales. A large ANN might have hundreds or thousands of processor units, whereas a mamalian brain has billions of neurons with a corresponding increase in magnitude of their overall interaction and emergent behavior. Although ANN researchers are generally not concerned with whether their networks accurately resemble biological systems, some have. For example, researchers have accurately simulated the function of the retina and modeled the eye rather well.
Although the mathematics involved with neural networking is not a trivial matter, a user can rather easily gain at least an operational understanding
of their structure and function.
The Basics of Neural Networks
Neural neworks are typically organized in layers. Layers are made up of a number of interconnected 'nodes' which contain an 'activation function'.
Patterns are presented to the network via the 'input layer', which communicates to one or more 'hidden layers'
where the actual processing is done via a system of weighted 'connections'.
The hidden layers then link to an 'output layer' where the answer is output as shown in the graphic below.
Most ANNs contain some form of 'learning rule' which modifies the weights of the connections according to the input patterns that it is presented with. In a sense, ANNs learn by example as do their biological counterparts; a child learns
to recognize dogs from examples of dogs.
Although there are many different kinds of learning rules used by neural networks, this demonstration is concerned only with one; the delta rule.
The delta rule is often utilized by the most common class of ANNs called 'backpropagational neural networks' (BPNNs). Backpropagation is an abbreviation for the backwards propagation of error.
For other uses, see Neural network (disambiguation).
Structure in biology and artificial intelligence
Simplified view of a feedforward artificial neural network
A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus a neural network is either a biological neural network, made up of real biological neurons, or an artificial neural network, for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled as weights. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1.
These artificial networks may be used for predictive modeling, adaptive control and applications where they can be trained via a dataset. Self-learning resulting from experience can occur within networks, which can derive conclusions from a complex and seemingly unrelated set of information.
A biological neural network is composed of a groups of chemically connected or functionally associated neurons. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive.
Connections, called synapses, are usually formed from axons to dendrites, though dendrodendritic synapses and other connections are possible.
Apart from the electrical signaling, there are other forms of signaling that arise from neurotransmitter diffusion.
Artificial intelligence, cognitive modeling, and neural networks are information processing paradigms inspired by the way biological neural systems process data.
Artificial intelligence and cognitive modeling try to simulate some properties of biological neural networks.
In the artificial intelligence field, artificial neural networks have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct software agents (in computer and video games) or autonomous robots.
An artificial neural network learning algorithm, or neural network, or just neural net
, is a computational learning system that uses a network of functions to understand and translate a data input of one form into a desired output, usually in another form. The concept of the artificial neural network was inspired by human biology and the way neurons of the human brain function together to understand inputs from human senses.
Neural networks are just one of many tools and approaches used in machine learning algorithms. The neural network itself may be used as a piece in many different machine learning algorithms to process complex data inputs into a space that computers can understand.
Neural networks are being applied to many real-life problems today, including speech and image recognition, spam email filtering, finance, and medical diagnosis, to name a few.
How Does a Neural Network Work?
Machine learning algorithms that use neural networks generally do not need to be programmed with specific rules that define what to expect from the input. The neural net learning algorithm instead learns from processing many labeled examples (i.e.
data with with “answers”) that are supplied during training and using this answer key to learn what characteristics of the input are needed to construct the correct output. Once a sufficient number of examples have been processed, the neural network can begin to process new, unseen inputs and successfully return accurate results.
The more examples and variety of inputs the program sees, the more accurate the results typically become because the program learns with experience.
This concept can best be understood with an example. Imagine the “simple” problem of trying to determine whether or not an image contains a cat.
While this is rather easy for a human to figure out, it is much more difficult to train a computer to identify a cat in an image using classical methods.
Considering the diverse possibilities of how a cat may look in a picture, writing code to account for every scenario is almost impossible.
But using machine learning, and more specifically neural networks, the program can use a generalized approach to understanding the content in an image. Using several layers of functions to decompose the image into data points and information that a computer can use, the neural network can start to identify trends that exist across the many, many examples that it processes and classify images by their similarities.
Neural Network Definition
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.
Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria.
The concept of neural networks, which has its roots in artificial intelligence, is swiftly gaining popularity in the development of trading systems.
Image by Sabrina Jiang © Investopedia 2020
- Neural networks are a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data.
- They are used in a variety of applications in financial services, from forecasting and marketing research to fraud detection and risk assessment.
- Use of neural networks for stock market price prediction varies.
Neural networks, in the world of finance, assist in the development of such process as time-series forecasting, algorithmic trading, securities classification, credit risk modeling and constructing proprietary indicators and price derivatives.
A neural network works similarly to the human brain’s neural network. A “neuron” in a neural network is a mathematical function that collects and classifies information according to a specific architecture. The network bears a strong resemblance to statistical methods such as curve fitting and regression analysis.
A neural network contains layers of interconnected nodes. Each node is a perceptron and is similar to a multiple linear regression. The perceptron feeds the signal produced by a multiple linear regression into an activation function that may be nonlinear.
In a multi-layered perceptron (MLP), perceptrons are arranged in interconnected layers. The input layer collects input patterns. The output layer has classifications or output signals to which input patterns may map. For instance, the patterns may comprise a list of quantities for technical indicators about a security; potential outputs could be “buy,” “hold” or “sell.”
Hidden layers fine-tune the input weightings until the neural network’s margin of error is minimal. It is hypothesized that hidden layers extrapolate salient features in the input data that have predictive power regarding the outputs. This describes feature extraction, which accomplishes a utility similar to statistical techniques such as principal component analysis.
Neural networks are broadly used, with applications for financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks have also gained widespread adoption in business applications such as forecasting and marketing research solutions, fraud detection and risk assessment.
What Is a Neural Network?
Common machine learning techniques for designing neural network applications include supervised and unsupervised learning, classification, regression, pattern recognition, and clustering.
Supervised neural networks are trained to produce desired outputs in response to sample inputs, making them particularly well suited for modeling and controlling dynamic systems, classifying noisy data, and predicting future events. Deep Learning Toolbox™ includes four types of supervised networks: feedforward, radial basis, dynamic, and learning vector quantization.
Classification is a type of supervised machine learning in which an algorithm “learns” to classify new observations from examples of labeled data.
Regression models describe the relationship between a response (output) variable and one or more predictor (input) variables.
What is Neural Network: Overview, Applications, and Advantages
Do you wonder how Google Assistant or Apple’s Siri follow your instructions? Do you see advertisements for products you earlier searched for on e-commerce websites? If you have wondered how this all comes together, it is because of Artificial Intelligence (AI), which works on the backend to offer you rich customer experience. And it is Artificial Neural Networks (ANN) that form the key to train machines to respond to instructions the way humans do.
This article dives deep into the fundamental concepts of neural networks, including:
- What is deep learning?
- What is a neural network?
- How does a neural network work?
- Advantages of neural networks
- Applications of neural networks
- The future of neural networks
A Brief History of AI
The human brain is the most complex organ in the human body. It helps us think, understand, and make decisions. The secret behind its power is a neuron.
Ever since the 1950s, scientists have been trying to mimic the functioning of a neuron and use it to make smarter and better robots.
After a lot of trial and error, humans finally created a computer that could recognize human speech.
It was only after the year 2000 that people were able to master deep learning (a subset of AI) that was able to see and distinguish between various images and videos.
Before taking a detailed look at what is a neural network, you should be aware of deep learning.
What is Deep Learning?
Deep learning is a subset of machine learning that asks computers to do what comes naturally to humans: learn by example.