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Difference between Deep Learning and Machine Learning is shown below:
Parameters Machine Learning Deep Learning
Data Dependency Machine Learning algorithm can easily work When the size of the data is
with smaller data set. small, a Deep Learning algorithm
does not perform well as a deep
learning algorithm needs large
amounts of data to understand
perfectly.
Hardware Dependency Machine Learning algorithms can work on Deep Learning algorithms are heavily
low end machines as well. dependent on high-end machines.
Problem Solving When we are solving a problem using a Deep Learning algorithm solves the
Approach traditional machine learning algorithm it is problem end to end.
generally recommended that we first break
down the problem into different sub parts
and solve them individually and then finally
combine them to get the desired result.
Execution Time Machine Learning algorithms take much less Usually, Deep Learning algorithms
time to train. take a long time to train because
there are many parameters making
the training time longer than usual.
ANN
ANN stands for Artificial Neural Network. It is made up of neurons that help in image recognition and image
processing. It uses Deep Learning algorithm that takes an input image, processes it by assigning learnable weights
and biases to various aspects/objects in the image which will help the system to differentiate one image from the
other with maximum accuracy. We can say that ANN reduces size, improves features and gives the probability
value of the input image. They are trained to identify and extract the best features from the images.
Car
Input Artificial Neural Network Output
Real-world examples of Deep Learning
Let us learn about some real-world examples of Deep Learning:
• Object Identification: Deep learning is used in identifying and classifying objects, animals, or scenes in images.
Its application can be seen in Google Photos where it automatically categorises images. And in medical imaging
for detecting diseases like tumors.
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