Provides a concise introduction about deep learning

1. Deep Learning Techniques
Dr.R.R.Rajalaxmi
Professor/CSE
Kongu Engineering College
Perundurai

2. Introduction
• AI - simulation of human intelligence in machines that are programed
to think like humans and mimic their actions
• ML
• The field of study that gives computers the ability to learn without being
explicitly programmed
• DL-broader family of machine learning methods based on artificial
neural networks with representation learning
• DM vs ML
• data mining - “extracting information from data.
• ML – algorithms used during data mining for acquiring the structural
descriptions from the raw data
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3. History of DL
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4. Deep
Learning ?
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5. Relationship between AI,ML and DL
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6. ML vs DL
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7. Deep Learning Applications
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8. Biological vs Artificial Neuron
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9. Neural Network vs Deep Learning
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10. Deep learning
Definition: neural networks with a large number of parameters and layers in
one of four fundamental network architectures:
• Unsupervised pretrained networks
• Convolutional neural networks
• Recurrent neural networks
• Recursive neural networks
• Advantages
• Automatic feature extraction is one of the great advantages that deep learning has
over traditional machine learning algorithms
• surpassed those conventional algorithms in accuracy for almost every data type with
minimal tuning and human effort
• help data science teams save their blood, sweat, and tears for more meaningful
tasks.
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11. Applying machine learning
• best understood by asking the correct questions to begin with
• What is the input data from which we want to extract information (model)?
• What kind of model is most appropriate for this data?
• What kind of answer would we like to elicit from new data based on this
model?
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12. References
1. Josh Patterson and Adam Gibson, “Deep Learning – A
Practitioner’s Approach”, First Edition, O’Reilly Series,
August-2017
2. Ian Goodfellow ,Yoshua Bengio and Aaron Courville, “Deep
Learning”, First Edition, MIT Press, 2016
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