What is Artificial intelligence,machine Learning and Deep Learning.

1. Deep Learning
Muhammad Adnan Shakeel 147024
Department Of Computer Science And Information Technology

2. Agenda:
 Brief History
 Artificial Intelligence
 Machine Learning
 Methods OF Learning
 Limitations of Machine Learning
 Deep Learning
 Applications Of Deep Learning
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3. Brief History:
 The theoretical concept of AI (1956).
 Concept of Neural Networks ( 80’s & 90’s).
 Use of Neural Networks for Machine Learning (late 90’s & 2000).
 Deep Learning was 1st confined in 2006 to over come the Limitations of Machine Learning.
 In 2010 Deep learning was used commercially.
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4. Subsets Of Artificial Intelligence:
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5. Artificial Intelligence:
Artificial Intelligence is the capability of a machine to imitate intelligence human behavior.
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6. Applications Of Artificial Intelligence:
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7. Machine Learning:
 Machine Learning is a type of AI that provides computer with ability to learn without being explicitly programed.
 Problem Statement: determine the specie Of Flower.
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8. Types Of Machine Learning:
Supervised Learning:
 Can apply what has been learned in the past to new data using labeled examples to predict future events.
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9. Unsupervised Learning:
 It is the training of a module using information neither classified
nor labelled.
 This module can be used to cluster the input data in classes on the
basis of their statistical properties.
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10. Reinforcement Learning:
 It is learning by interacting with environment or space.
 An RL learns from Consequences of its action rather from being taught explicitly. It selects its action on basis
of its past experience and also by new choices.
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11. Limitations of Machine Learning:
 ML is not useful while working with high dimensional data that is where
we have large number of inputs.
 Cannot solve crucial AI problems like, NLP , image recognition etc.
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12. Limitations cont.…
 One of big challenges with traditional machine learning models is a process called feature extraction.
 For complex problems such as object recognition or handwriting recognition , this is a huge
challenge.
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13. Deep learning is to rescue:
 Deep learning models are capable to focus on the right features by themselves requiring little
guidance from the programmer.
 These models also partially solve the dimensionally problems.
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14. Cont.…
 Deep learning is implemented through neural networks
 Motivation behind Neural networks is the biological neuron.
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15. What is Deep Learning:
 A collection of statistical machine learning techniques used tp learn features hierarchies often based on
artificial neural networks.
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16. Deep Learning Example:
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17. Application Of deep Learning:
 Machine Translation.
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18.  Object classification In Photographs:
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19.  Google Lens:
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20. Session in a Minute:
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