What Is A Neural Network
Publisher: Psychz Networks, June 19,2019Introduction
Imagine if there is a technology that could use its own capability to write codes, reprogram itself or even repair making it so simple and helpful for mankind. Any failed program or bugs in the system would repair itself and we never have to upgrade our system or software. However, such ideas were appealing but very difficult to implement in the initial days. Promises went unfulfilled, and at times greater philosophical questions led to fear. Writers pondered the effect that the so-called "thinking machines" would have on humans, ideas which are still around today.
Today, the most common examples of Artificial Intelligence that we hear are - from Chess-playing computers to Self-driving cars that heavily rely on Neural networks, Deep learning, and Natural language processing. With the help of these technologies, we can train computers to accomplish tasks that are highly specific in nature by processing a large amount of data and recognizing patterns in the data. Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks.
While there are many Hollywood movies and tons of science fiction novels that depict AI as human-like robots that can take over the world. The idea is haunting and scary but the current evolution of AI technologies isn’t that scary or quite that smart for that matter. Instead, AI has evolved to provide many specific benefits in every industry.
In this article, we will cover the following topics
- What is a Neural Network?
- Types of Neural Network?
- What is Deep Learning?
- Example of an Artificial Neural Network
A neural network is a complex system which is adaptive in nature. This means that it can change its internal structure based on the information flowing through it. Neural networks are designed in such a way that they can adapt to changing the input and is capable of generating the best possible result without needing to redesign the output criteria. In other words, Artificial Neural Networks (ANNs) are software implementations of the neuronal structure of our brains.
A human brain contains neurons which are similar to organic switches which can change the output depending on the strength of the electrical signal passed in the form of chemical. The complexity of this network is very high because it is a huge network of interconnected neurons where the output of any given neuron maybe input to thousands of other neurons.
In an Artificial Neural Network, each neuron forms a weighted sum of the inputs from previous layers to which it is connected, adds a threshold value, and produces a nonlinear function of this sum as its output value. This output value serves as input to the future layers to which the neuron is connected, and the process is repeated. Ultimately value is produced for the outputs of the neurons in the Output layer.
A Neural Network can be designed and trained in a Supervised or Unsupervised manner. In the supervised method, the network is trained by providing matched input and output data samples. This helps the neural network to provide the specific output for a given input. A very apt example in today's world can a modern email spam filter which uses filters where you can input desired keywords which it can match with the body of the email and then classify it as spam or not based on the input fed. If a large set of data is fed in the form of emails the neural network learns and identifies spams with much more efficiency and success rate. This learning takes place by adjusting the weights of the ANN connections.
In Unsupervised learning, the neural network draws inferences from datasets consisting of input data without labeled responses. The Unsupervised learning attempts to get the neural network to “understand” the structure of the provided input data “on its own”. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data. The clusters are modeled using a measure of similarity which is defined upon metrics such as Euclidean or probabilistic distance.
Deep learning, often referred to as Deep Neural Networks because most of its methods use neural network architectures. The number of hidden layers in the neural network in Deep learning is very high as compared to a traditional neural network. A neural network may contain only 2 - 3 hidden layers, whereas a deep network can have as many as 150 layers.
Deep learning is a specialized form of machine learning. Where in the workflow, the relevant features are extracted manually in a machine learning and then used to create a model that is used to categorize the objects. On the other hand, with Deep learning, all the features are extracted automatically. In addition, deep learning performs “end-to-end learning” – where a network is given raw data and a task to perform, such as classification, and it learns how to do this automatically. Deep learning models are trained by using large sets of labeled data and neural network architectures that learn features directly from the data without the need for manual feature extraction.
A key advantage of deep learning networks is that they often continue to improve as the size of your data increases. Which means that deep learning algorithms scale with data.
Application of Neural Network can be infinite because of its capability to learn, adjust and re-structure itself to meet the desired output makes it so useful in the area of artificial intelligence. Following are some of the most common applications of neural network in today's world
Soft Sensors —A soft sensor is a process of analyzing a collection of various measurements. A thermometer can tell you the temperature of the air, but what if you also knew the humidity, barometric pressure, dewpoint, air quality, air density, etc.? Neural networks can be employed to process the input data from many individual sensors and evaluate them as a whole.
Signal Processing —Neural Networks can have endless application in the medical field because of its capability to process as humans. For example, they can be useful for hearing aids as it needs to filter out unnecessary noise and amplify the important sounds. Neural networks can be trained to process an audio signal and filter it appropriately.
Time Series Prediction —Neural networks can be used to make predictions. Predictions based on patterns and past historical data. This can be used to predicts if the stock rise or fall tomorrow? Will it rain or be sunny?
Pattern Recognition —Pattern recognition is probably the most common and widely used area for Neural network. The technology is used for facial recognition, optical character recognition, etc.
Control Systems— A highly promising area where some of the most renowned automobile manufacturers are using the neural network to develop self-driving cars.
Above are some of the applications of neural networks and the list is by no means comprehensive. There are endless possibilities of a neural network but hopefully, the list will give you an overall sense of the features and possibilities. While this is all fascinating (and incredibly important to scientific research), a lot of the techniques are not very practical in the world of building interactive, animated Processing sketches.