dep learnig

1. DEEP LEARNING
BY ABISHEK R
RA2112039010002
M.TECH BIG DATAANALYTICS
20PDM601T Career Advancement Course for Engineers - III
Tuesday, October
11, 2022
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2. Topics to be Covered
• Introduction: Deep Learning
• Machine learning vs deep learning
• Architecture of deep learning
• Deep learning models
• Applications of deep learning
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3. Introduction
• Deep Learning is the subset of machine learning
• It works technically in the same way as machine learning does, but with different
capabilities and approaches.
• Deep learning models are capable enough to focus on the accurate features
themselves by requiring a little guidance from the programmer.
• Deep learning is implemented with the help of Neural Networks, and the idea
behind the motivation of neural network is the biological neurons, which is nothing
but a brain cell.
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4. Machine LearningVs Deep Learning
• Machine Learning and Deep Learning are the two main concepts of Data
Science and the subsets of Artificial Intelligence.
• Most of the people think the machine learning, deep learning, and as well as
artificial intelligence as the same buzzwords. But in actuality, all these terms
are different but related to each other
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5. How Machine learning differs from Deep
learning
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6. Architecture of Deep learning
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7. Deep Learning models
• Some popular deep learning models are:
• Convolutional Neural Network (CNN)
• Recurrent Neural Network (RNN)
• Autoencoders
• Classic Neural Networks, etc.
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8. Deep Learning Applications
• Self-Driving Cars
• Voice Controlled Assistance
• Automatic Image Caption Generation
• Automatic MachineTranslation
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9. Conclusion
• DL debugging is hard
• Needs huge computation power
• Computation time is still an issue
• No theoretical proof for optimal parameters
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10. Thankyou
Questions ?
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11, 2022
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