Introduction to Artificial Intelligence
Artificial Intelligence
what is Artificial Intelligence
Artificial
Intelligence includes the and special e simulation
process of human
intelligence by machines computer systems.
The examples of
artificial intelligence include
learning, reasoning and self correction. Applications
of AI include
speech recognition, expert systems, and
image recognition on and
machine vision. Machine learning
is the branch
of artificial intelligence, which algorithms that can
learn any new data
and data patterns. Let us deals
with systems and focus
on the Venn
diagram mentioned below
for understanding machine
learning and deep learning concepts.
Machine learning includes
a section of
machine learning and
deep learning is
a part of machine
learning. The ability
of program which
follows machine learning
concepts is to improve
its performance of
observed data. The main
motive of data
transformation is to improve
its knowledge in
order to achieve
better results in the future,
provide output closer to
the desired output
for that particular
system. Machine learning
includes “pattern recognition” which
includes the ability
to recognize the
patterns in data. The patterns
should be trained
to show the output
in desirable manner. Machine learning can be trained in two different way
1:Supervised training
2:Unsupervised training
Supervised Learning
Supervised learning or supervised training includes a procedure where the training set is given as input to the system wherein resents the , each example is labeled with a desired output value. The training in this type is performed using minimization of a particular loss function, which rep output error with respect to the desired output system. After completion of training, the accuracy of each model is measured with respect to disjoint examples from training set, also called the validation set.
The best example
to illustrate “ information
included in them Supervised learning”
is with . Here,
the user can train a bunch of
photos given with a model
to recognize new
photos
Unsupervised learning
In unsupervised learning
or unsupervised training,
include training not labeled
by the system to
which class they
belong share common characteristics, and changes examples, which .
The system looks
for the are data, which them
based on internal
knowledge features. This type
of learning algorithms are basically
used in clustering
problems.
The best example to
illustrate “Unsupervised learning”
is with bunch of
photos with no information included
and user trains
model with classification and
clustering . This type
of training algorithm works with
assumptions as no
information is given.
Machine Learning
Machine learning is the art of science of getting computers to act as per the algorithms designed and programmed. Many researchers think machine learning is the best way to make progress
towards human-level AI. Machine learning includes the following types of patterns:
✦Supervised learning pattern
✦Unsupervised learning pattern
Deep Learning
Deep learning is a subfield of machine learning
where concerned algorithms are inspired by the structure and function of the brain called
artificial neural networks.
All the value today of deep learning is through supervised learning or learning from labelled data and algorithms.
Each algorithm in deep learning goes through the same process. It includes a hierarchy of nonlinear transformation of input that can be used to generate a statistical model as output.
Consider the following steps that define the Machine Learning process:
⬥ Identifies relevant data sets and prepares them for analysis.
⬥ Chooses the type of algorithm to use.
⬥ Builds an analytical model based on the algorithm used.
⬥ Trains the model on test data sets, revising it as needed.
⬥ Runs the model to generate test scores.
Difference between Machine Learning and Deep learning
In this section, we will learn about the difference between Machine Learning and Deep Learning.
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