Deep learning and neural network converted

Deep Learning and Neural Network

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Deep Learning and Neural Network :
You can think of it – how a child learns through constant experiences and
replication. Deep learning and Neural Network could provide unexpected business
models for companies.
We know that computers are better than people at crunching series of numbers or
faster processing of monotonous job, but what about tasks that are more complex?
How do you teach a computer what a Dog looks like? Or how to drive a car? Or how
to play a complex strategy game? Or make predictions about the stock market?
These are some of the most difficult tasks in artificial intelligence, far outstripping
the capabilities of normal machine learning techniques. In these cases, computer
scientists turn to neural networks and deep learning.

1.Deep Learning :
Deep learning, also known as the deep neural network, is one of the approaches to
machine learning. Deep learning is a special type of machine learning. Deep learning
involves the study of Artificial Neural Networks and Machine Learning related
algorithms that contain more than one hidden layer. A deep learning model is
designed to continually analyse data with a logic structure similar to how a human
would draw conclusions. Deep learning involves mathematical modelling, which can
be thought of as a composition of simple blocks of a certain type, and where some of
these blocks can be adjusted to better predict the final outcome. Deep learning is a
subfield of machine learning. While both fall under the broad category of artificial
intelligence, deep learning is what powers the most human-like artificial intelligence.

2. Neural Network:
Neural networks are a set of algorithms, modelled loosely after the human brain,
that are designed to recognize patterns. Neural networks help us cluster and classify.
Within the field of machine learning, neural networks are a subset of algorithms
built around a model of artificial neurons spread across three or more layers. They
interpret sensory data through a kind of machine perception, labelling or clustering
raw input. The patterns they recognize are numerical, contained in vectors, into
which all real-world data, be it images, sound, text or time series, must be
translated. Neural networks can also extract features that are fed to other
algorithms for clustering and classification; so you can think of deep neural networks
as components of larger machine-learning applications involving algorithms for
reinforcement learning, classification and regression.

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3. Applications of Neural Network:
Image Processing and Character recognition: Neural Networks are playing a big 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 facial recognition in social media, cancer detention in medicine to satellite
imagery processing for agricultural and defence usage.
Signature Verification Application: Signatures are one of the most useful ways to
authorize and authenticate a person in legal transactions. With these feature sets, we
have to train the neural networks using an efficient neural network algorithm.
Human Face Recognition : It is one of the biometric methods to identify the given
face. It is a typical task because of the characterization of ‘non-face’ images.

Forecasting: Forecasting is required extensively in everyday business decisions.
Forecasting includes sales, financial allocation between products, capacity
utilization, in economic and monetary policy, in finance and stock market. More
often, forecasting problems are complex, for example, predicting stock prices.
Character Recognition: It is an interesting problem which falls under the general
area of Pattern Recognition. Many neural networks have been developed for
automatic recognition of handwritten characters, either letters or digits.
Speech Recognition: Speech occupies a prominent role in human-human
interaction. Therefore, it is natural for people to expect speech interfaces with
computers.

4. Applications of Deep Learning:
Self-driving cars: Companies are on the way to launch self-driving cars. They are
using different digital sensor systems and to do that companies generally start out
by training algorithms using a large amount of data.
Healthcare Sector: Breast or Skin-Cancer diagnostics? Mobile and Monitoring
Apps? or prediction and personalised medicine on the basis of Biobank-data? AI is
completely reshaping life sciences, medicine, and healthcare as an industry.
Voice Search & Voice-Activated Assistants: Google Home, Alexa, Cortona, Siri are
already assisting large number of users in their personal life. They are the result of
deep learning is voice search & voice-activated intelligent assistants.

Deep learning is automatically Adding Sounds To Silent Movies
Automatic Text Generation and Handwriting Generation: Using deep learning,
corpus of text is learned and from this model new text is generated, word-by-word or
character-by-character. The model is capable of learning how to spell, punctuate, form
sentences and even capture the style of the text in the corpus.
Image Recognition: Another popular area regarding deep learning is image
recognition. It aims to recognize and identify people and objects in images as well as
to understand the content and context.

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5.Difference Between Deep Learning and Neural Network :
Neural Network Deep Learning
It is a beautiful biologically-
inspired programming paradigm
which enables a computer to
learn from observational data
It is a powerful set of techniques for
learning in neural networks
Neural networks make use of
neurons that are used to
transmit data in the form of
input values and output values.
They are used to transfer data
by using networks or
connections.
Deep learning is related to transformation
and extraction of feature which attempts
to establish a relationship between
stimuli and associated neural responses
present in the brain.

Application areas for neural networks
includes system identification, natural
resource management, process control,
vehicle control, quantum chemistry,
decision making, game playing
Application of deep learning includes Automatic
speech recognition, Image recognition, visual art
processing, Natural language processing, drug
discovery and toxicology, customer relationship
management, recommendation engines
Neural Network has two layers Deep learning, you can have many layers

6. Advantages of Neural Network:
•A neural network learns and does not need to be reprogrammed.
•A neural network can be implemented in any application.
•If any element of neural networks fails, it can continue without any problem by
their parallel nature
•A neural network can perform tasks that a linear program cannot.
•A neural networks requires high processing time for large neural networks.

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Deep learning and neural network converted

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Deep learning and neural network converted