This document provides an overview of deep learning, including definitions, origins, applications, and limitations. It defines deep learning as a machine learning technique that uses multiple layers to learn representations of data. Deep learning algorithms attempt to learn multiple levels of representation using a hierarchy of layers. While deep learning has been used since around 2000, it has grown as a subset of machine learning focused on deep artificial neural networks. Deep learning can learn both unsupervised and supervised and is useful for tasks like speech recognition, natural language processing, image recognition, and self-driving cars. However, it requires large amounts of data and time to train models.

1. BASICS OF DEEP LEARNING
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Made by
Sunil kumar pandit

2. Contents
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 What is Deep leaning?
 Origin of Deep learning
 Why is DL used?
 Al vs ML vs DL
 Deep neural network
 Application
 Limitation

3. What is Deep Learning ?
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 A machine learning subfield of learning representations of data. Exceptional
effective at learning patterns.
 Deep learning algorithms attempt to learn (multiple levels of) representation
by using a hierarchy of multiple layers

4. Origin of Deep Learning
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 If machine learning is a subfield of artificial
intelligence, then deep learning could be called a
subfield of machine learning. The evolution of the
subject has gone artificial intelligence > machine
learning > deep learning.
 The expression “deep learning” was first used when
talking about Artificial Neural Networks (ANNs) by
Igor Aizenberg and colleagues in or around 2000.

5. Why is DL used ?
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o Learned Features are easy to adapt, fast to learn
o Deep learning provides a very flexible, universal, learnable framework for
representing world, visual and linguistic information.
o Can learn both unsupervised and supervised
o Utilize large amounts of training data

6. AI vs ML vs DL
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 Artificial intelligence is the study of how to build
machines capable of carrying out tasks that would
typically require human intelligence.
 Machine learning is the process of teaching a
computer to carry out a task, rather than
programming it how to carry that task out step by
step.
 Deep learning is a subset of machine learning.
Usually , when people use term deep learning, they
are referring to deep artificial neural network .

7. AI vs ML vs DL
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8. Deep Neural Network
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 A deep neural network (DNN) is an artificial neural
network (ANN) with multiple layers between the
input and output layers. The DNN finds the correct
mathematical manipulation to turn the input into
the output, whether it be a linear relationship or a
non-linear relationship. The network moves through
the layers calculating the probability of each output

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10. Application
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 Automatic speech recognition

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 Natural language processing

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 Image recognition

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 Self-driving cars

14. Limitation
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 I think the biggest obstacle to Deep Learning is the
need to have HUGE amount of data.
 It is very time-consuming .
 The main limitation and challenging task in Deep
learning is to reduce the complicity of network. In
normal deep learning technique there are more than
20 layers.

15. Thank you
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