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1.Introduction to deep learning

1) Deep learning is a type of machine learning that uses neural networks with many layers to learn representations of data with multiple levels of abstraction. 2) Deep learning techniques include unsupervised pretrained networks, convolutional neural networks, recurrent neural networks, and recursive neural networks. 3) The advantages of deep learning include automatic feature extraction from raw data with minimal human effort, and surpassing conventional machine learning algorithms in accuracy across many data types.

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Deep Learning Techniques Dr.R.R.Rajalaxmi Professor/CSE Kongu Engineering College Perundurai
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 7/23/2020 Introduction to Deep Learning 2
History of DL 7/23/2020 Introduction to Deep Learning 3
Deep Learning ? 7/23/2020 Introduction to Deep Learning 4
Relationship between AI,ML and DL 7/23/2020 Introduction to Deep Learning 5
ML vs DL 7/23/2020 Introduction to Deep Learning 6
Deep Learning Applications 7/23/2020 Introduction to Deep Learning 7
Biological vs Artificial Neuron 7/23/2020 Introduction to Deep Learning 8
Neural Network vs Deep Learning 7/23/2020 Introduction to Deep Learning 9
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. 7/23/2020 Introduction to Deep Learning 10
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? 7/23/2020 Introduction to Deep Learning 11
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 7/23/2020 Introduction to Deep Learning 12