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Tuesday, June 8, 2021

Machine Learning Tutorial Series

 1. Introduction 

Basic Mathematics behind traditional data algorithms, Only topics would be explored for theoretical understanding and no code would be written for the same.


  1. Naive Bayes Classifier

  2. Term Frequency - Inverse Term Frequency 

  3. Cosine Similarity 

  4. Linear Regression 

 

2. Applications and breakdown,
  

  • Tabular Data

  • Images 

  • Audio

  • Video


This breakdown along with the detailed analysis on each component would help you figure out how to work on Machine Learning Models.

Most of the research papers whether in computer vision, speech and text need a very good understanding of four things. All of these require thorough understanding along with the code. The idea is to first process them in 3 line definition format and then experiment with the TensorFlow. 

  • Components

    • Recurrent Neural Networks

    • Convolutional Neural Networks 

    • Long Short Term Memory Cells

    • Gated Recurrent Units

  • Topology Used 

    • Encoder/Decoder

    • Bidirectional 

    • Grid LSTM

    • Tree LSTM

  • Additional factors

    • Attention

    • Normalization

    • Regularization 

    • Share/Unshare Something

  • Activation Function

    • TanH

    • Rectified Linear Unit

    • Parametric Rectified Linear Unit 

    • Exponential Linear Unit 

    • Sigmoid

    • Softplus

3. Conclusions and Rules of Thumb for practical purposes. 

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