In this article, we will be talking about neural networks. A functional unit of deep learning means a neural network accepts input and gives an output.
Deep Learning uses artificial neural networks(ANN) which become increasingly popular with their promising benefits.
ANNs imitates the human brains behavior to solve complex data problems
Using Deep learning Neural networks technology we can solve problems in image recognition, Speech recognition, pattern recognition, and natural language processing(NLP), etc
In this article, you will learn the basics of artificial neural networks or ANN. It will also give you an in-depth interpretation of how neural networks operate.
Overview
- Neural Networks
- How Neural Networks Work
- Types of Neural Networks
- Applications of Neural Networks
- Advantages of Neural Networks
- Disadvantages of Neural Networks
Neural Networks Overview
We know how the brain cells are connected and how our brain recognizes images, sounds, and understands.
Neural Networks learn models, identify patterns, and arrange non-identical kinds of information while trying to imitate the human brain.
No matter what or how the image looks, the brain can tell that this is an image of a cat or dog. the brain relates the best possible pattern and concludes the results.
The example below will help you understand neural networks
To create a neural network that recognizes images of cats and dogs. The network starts by processing the input. Each image is made of pixels.
For example, the image dimensions might be 20 X 20 pixels that make 400 pixels. Those 400 pixels would make the first layer of our neural network.
Neural Networks Consists:
- Input Layer
- Hidden Layer
- Output Layer
The input layer receives input data. Hidden layers perform mathematical computations on the information. Finally, the output layer gives the result. The problem is solved based on calculations from the distributive weights of all layers.
A neural network is made of artificial neurons that receive and process input data. Data is passed through the input layer, the hidden layer, and the output layer.
A neural network process starts when input data is fed to it. Data is then processed via its layers to provide the desired output.
A neural network learns from structured data and exhibits the output. Learning taking place within neural networks can be in three different categories:
- Supervised Learning — with the help of labeled data, inputs, and outputs are fed to the algorithms. They then predict the desired result after being trained on how to interpret data.
- Unsupervised Learning — ANN learns with no human intervention. There is no labeled data, and output is determined according to patterns identified within the output data.
- Reinforcement Learning — the network learns depending on the feedback give to the network.
The essential building block of a neural network is a perceptron or neuron. It uses the supervised learning method to learn and classify data. We will learn more about the perceptron later in this article.
How Neural Networks work
Neural Networks are complex systems with artificial neurons.
Artificial neurons or perceptrons consist of:
- Input
- Weight
- Bias
- Activation Function
- Output
The neurons receive many inputs and process a single output.
Neural networks are comprised of layers of neurons.
These layers consist of the following:
- Input layer
- Multiple hidden layers
- Output layer
The input layer receives data represented by a numeric value. Hidden layers perform the most computations required by the network. Finally, the output layer predicts the output.
In a neural network, neurons dominate one another. Each layer is made of neurons. Once the input layer receives data, it is redirected to the hidden layer. Each input is assigned with weights.
The weight is a value in a neural network that converts input data within the network’s hidden layers. Weights work by input layer, taking input data, and multiplying it by the weight value.
It then initiates a value for the first hidden layer. The hidden layers transform the input data and pass it to the other layer. The output layer produces the desired output.
The inputs and weights are multiplied, and their sum is sent to neurons in the hidden layer. Bias is applied to each neuron. Each neuron adds the inputs it receives to get the sum. This value then transits through the activation function.
The activation function outcome then decides if a neuron is activated or not. An activated neuron transfers information into the other layers. With this approach, the data gets generated in the network until the neuron reaches the output layer.
Another name for this is forward propagation. Feed-forward propagation is the process of inputting data into an input node and getting the output through the output node. (We’ll discuss feed-forward propagation a bit more in the section below).
Feed-forward propagation takes place when the hidden layer accepts the input data. Processes it as per the activation function and passes it to the output. The neuron in the output layer with the highest probability then projects the result.
If the output is wrong, backpropagation takes place. While designing a neural network, weights are initialized to each input. Backpropagation means re-adjusting each input’s weights to minimize the errors, thus resulting in a more accurate output.
Types of Neural Networks
Neural networks are identified based on mathematical performance and principles to determine the output. Below we will go over different types of neural networks.
Perceptron
Minsky and Papert proposed the Perceptron model (Single-layer neural network). They said it was modeled after how the human brain functions.
It is one of the simplest models that can learn and solve complex data problems using neural networks. A perceptron is also called an artificial neuron.
A perceptron network is comprised of two layers:
- Input Layer
- Output Layer
The input layer computes the weighted input for every node. The activation function is pertained to get the result as output.
Feed Forward Neural Network
In a feed-forward network, data moves in a single direction. It enters via the input nodes and leaves through output nodes.
This is a front propagation wave.
By moving data in one direction, there is no backpropagation. The backpropagation algorithm calculates the gradient of the loss function with consideration to weights in the network.
The input product sum and their weights are computed. The data later is transferred to the output.
A couple of feed-forward neural networks applications are:
- Speech Recognition
- Facial Recognition
Radial Basis Function Neural Network
Radial Basis Function Neural Networks (RBF are comprised of three layers:
RBF networks classify data based on the distance of any centered point and interpolation.
Interpolation resizes images. Classification is executed by estimating the input data where each neuron reserves the data. RBF networks look for similar data points and group them.
Recurrent Neural Network
Neural networks such as a feed-forward networks move data in one direction. This type of network has the disadvantage of not remembering the data in past inputs. This is where RNNs come into play. RNNs do not work like standard neural networks.
A Recurrent Neural Network (RNN) is a network good at modeling sequential data. Sequential data means data that follow a particular order in that a thing follows another.
In RNN, the output of the previous stage goes back in as an input of the current step. RNN is a feedback neural network.
Saving the output helps make other decisions.
Convolution Neural Network
Convolutional Neural Networks (CNN) are commonly used for image recognition. CNN’s contain three-dimensional neuron arrangements.
The first stage is the convolutional layer. Neurons in a convolutional layer only process information from a small part of the visual field (image). Input features in convolution are abstracted in batches.
The second stage is pooling. It reduces the dimensions of the features and, at the same time, sustains valuable data.
CNN's launch into the third phase (fully connected neural network) when the features get to the right granularity level.
At the final stage, the final probabilities are analyzed and decide which class the image belongs to.
This type of network understands the image in parts. It also computes the operations multiple times to complete the processing of the image.
Image processing involves conversion from RGB to a grey-scale. After the image is processed, modifications in pixel value aid in identifying the edges. The images also get grouped into different classes.
CNN is mainly used in signal and image processing. An article that may help shed some light on how general computer vision work is here.
Advantages of Neural Networks
Fault tolerance
In a neural network, even if a few neurons are not working properly, that would not prevent the neural networks from generating outputs.
Real-time Operations
Neural networks can learn synchronously and easily adapt to their changing environments.
Adaptive Learning
Neural networks can learn how to work on different tasks. Based on the data given to produce the right output.
Parallel processing capacity
Neural networks have the strength and ability to perform multiple jobs simultaneously.
Disadvantages of Neural Networks
Unexplained behavior of the network
Neural networks provide a solution to a problem. Due to the complexity of the networks, it doesn’t provide the reasoning behind “why and how” it made the decisions it made. Therefore, trust in the network may be reduced.
Determination of appropriate network structure
There is no specified rule (or rule of thumb) for a neural network procedure. A proper network structure is achieved by trying the best network, in a trial and error approach. It is a process that involves refinement.
Hardware dependence
The pieces of equipment of a neural network are dependent on one another. By which we mean, that neural networks require (or are highly dependent on) processors with adequate processing capacity.
Applications of Deep Learning
There is massive excitement about artificial intelligence and its subsets. Here are a few Deep Learning applications that will govern the world.
- Self-driving cars
Companies such as Google are building driver assistance services. They are also teaching computers how to use digital sensors. In the automotive sector, researchers and developers are working diligently on deep learning-based techniques for self-driving cars.
2. Natural Language Processing
Machines are taught to understand the complexities associated with languages and semantics. To achieve this, NLP through Deep Learning plays a significant role. NLP also catch linguistic nuances and frame appropriate responses.
3. Healthcare
Deep Learning is completely revolutionizing the healthcare and the medical industries. AI has enabled healthcare and medical industries to advance tremendously.
Clinical researchers use DL to find a cure for untreatable diseases. DL helps with a speedy diagnostic of dangerous conditions. Many cancer tests, such as the Pap test and Mammograms, use DL to examine cell images under a microscope.
4. Virtual assistants
Siri and Google Assistants are approved deep learning virtual assistants. Deep Learning enables virtual assistants to learn and understand commands given by a user. Virtual assistants then execute by providing the appropriate answer naturally.
Virtual assistants use Deep Learning to learn about the user, from what the user searches most.
5. Fraud detection
The banking and financial sectors are benefiting from Deep Learning to detect transaction fraud. Autoencoders in Tensorflow are being used to catch credit card fraud, thus saving a lot of money from fraudsters. Fraud prevention is done by recognizing patterns in customer transactions.
6. Image recognition
Image Recognition using Deep Learning aims to recognize and learn content in images. Deep Learning also seeks to understand (gather data from) the surrounding in the image. Image Recognition is used in the gaming industry and within social media.
7. Entertainment
Entertainment companies such as Netflix recommend to their viewers what they need to watch. Deep Learning enables the entertainment industry to understand consumers’ behaviors. Applying DL to the entertainment industry provides an exciting experience to clients.
Deep Learning is revolutionizing the filmmaking process. Cameras can learn body language and conduct voice synthesis. Deep Learning can also help to emulate someone’s voice in virtual characters.
Conclusion
Deep Learning is an extensible and complex field. It functions with the help of Neural Networks to mimic human behavior. DL solves complex data problems. Problems that present themselves in pattern recognition, image recognition, speech recognition, and NLP.
This helps save people time as they do not have to perform repetitive actions or tasks. DL is being applied in many industries like healthcare, entertainment, financial sectors, etc. It is being used to solve different problems and reduce the risk of human error on many repetitive tasks.
The neural network field is growing tremendously. Learning and understanding the concepts in this field is vital in order to be able to work with them. This article has explained the different kinds of neural networks. By exploring this field, you can apply neural networks in other areas to solve data problems.
