What are Neural Networks?
Neural Networks are a category of models within the overall
machine learning literature. Neural networks are a selected set of algorithms
that have revolutionized machine learning. they're inspired by biological
neural networks and therefore the current so-called deep neural networks have
proven to figure quite well. Neural Networks are themselves general function
approximations, which is why they will be applied to almost any machine
learning problem about learning a posh mapping from the input to the output
space.
Neural Network(NN)
uses the processing of the brain as a basis to develop algorithms which will be
wont to model complex patterns and prediction problems.
Need for Neural Networks :
Neural networks have an interesting ability to retrieve meaningful
data from imprecise data, that's utilized in detecting trends and extract
patterns which are difficult to know either by computer or humans. A trained NN
are often made an "expert" in information that has been given to
analyse and may be used for provide projections.
Key advantages of Neural Networks:
NNs have some key advantages that make them best suited surely
problems and situations:
Self-Organisation: A NN can generate its own representation of
the knowledge that it receives at the time of learning.
Real Time Operation: NN calculations could also be done
simultaneously, and a few special (hardware) devices are manufactured which
cash in of this capability.
Adaptive learning: Capability to find out the way to solve
tasks is predicated on the info given for training set.
Redundant Information Coding Through Fault Tolerance: Semi
destruction of a network results in degradation of corresponding performance.
Moreover, some network will have the power to retain data even when a serious
network damage occurs.
● NNs have the power to find out and model non-linear and
sophisticated relationships, which is basically important because in real-life,
many of the relationships between inputs and outputs are non-linear also as
complex.
● NNs can generalize — After learning from the initial inputs
and their relationships, it can infer unseen relationships on unseen data also,
thus making the model generalize and predict on unseen data.
● Unlike many other prediction techniques, NN doesn't impose
any restrictions on the input variables (like how they ought to be
distributed). Additionally, many studies have shown that NNs can better model
heteroskedasticity i.e. data with high volatility and non-constant variance,
given its ability to find out hidden relationships within the data without
imposing any fixed relationships within the data. this is often something very
useful in financial statistic forecasting (e.g. stock prices) where data
volatility is extremely high.
Applications :
NNs, thanks to a number of its wonderful properties have many
applications:
1. Image Processing and Character recognition: Given NNs
ability to require during a lot of inputs, process them to infer hidden also as
complex, non-linear relationships, NNs are playing an enormous role in image
and character recognition. Character recognition like handwriting has lot of
applications in fraud detection (e.g. bank fraud) and even national security
assessments. Image recognition is an ever-growing field with widespread
applications from face recognition in social media, cancer detention in
medicine to satellite imagery processing for agricultural and defense usage.
The research on NN now has paved the way for deep neural
networks that forms the idea of “deep learning” and which has now opened all
the exciting and transformational innovations in computer vision, speech
recognition, tongue processing — famous examples being self-driving cars.
2. Forecasting: Forecasting is required extensively in everyday
business decisions (e.g. sales, financial allocation between products, capacity
utilization), in economic and monetary policy, in finance and stock exchange.
More often, forecasting problems are complex, for instance, predicting stock
prices may be a complex problem with tons of underlying factors (some known,
some unseen).
NNs are powerful models that have a good range of applications.
Above, we've listed a couple of prominent ones, but they need far-reaching
applications across many various fields in medicine, security, banking/finance
also as government, agriculture and defense.
Neural networks are one among the foremost beautiful
programming paradigms ever invented. within the conventional approach to
programming, we tell the pc what to try to to and break big problems up into
many small, precisely defined tasks that the pc can easily perform. In
contrast, we don’t tell the pc the way to solve our problems for a neural
network. Instead, it learns from observational data and figures out its own
solution to the matter.
Today, deep neural networks and deep learning achieve outstanding
performance for several important problems in computer vision, speech
recognition, and tongue processing. They’re being deployed on an outsized scale
by companies like Google, Microsoft, and Facebook. We hope that this blog helps
learn the core concepts of neural networks, including modern techniques for
deep learning.
