This document discusses deep learning and its applications. It provides a brief history of artificial intelligence and machine learning, noting that deep learning emerged to overcome limitations of machine learning. It describes types of machine learning like supervised, unsupervised, and reinforcement learning. Deep learning uses neural networks to focus on relevant features without much programmer guidance, helping to solve problems involving high-dimensional data like image recognition. Applications of deep learning discussed include machine translation, object classification in photos, and Google Lens.

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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