Deep Learning

Re-Imagined

Course Outline

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Week 1
    Introduction
    Orientation
    Precursors
    Supplemental information
        Previous ML course
        Basic Machine Learning
        Python
        Some mathematics
            Matrix Multiplication
            Derivatives
        Background in descriptive statistics
        Material
            Book
            Articles for each Week
    Motivation
    Homework
    Project/Capstone
    Platform
        

 

Week 2
    Neural Network Architecture
    Basic Python implementation
        Feed Forward
        Backpropagation
    Interpretability/Explainability

 

Week 3
    High level:
        Tensorflow
        Keras
        Pytorch
        Fast.AI
    Hyperparameter Tuning

 

Week 4
    MLP (Deeeeeep Learning)
        Tuning
    Interpreting/Explanability 2-layer
    Visualization tools

 

Week 5 - Visual Applications
    Convolutional Neural Networks (CNN)
        Tune via architechure
    Applications
    Interpreting/Explanability    
    Transfer learning

 

Week 6 - Sequencing Architectures
    Architectures

       Recurrent Neural Networks (RNN)
       Long Short Term Memory (LSTM)

       Gated Recurrent Units (GRU)

    Applications
    Interpretability
    Tuning mechanisms
    Transfer Learning

 

Week 7 - Advanced Architectures
    Combination CNN/RNN
    Applications/Examples
        Image captioning
    Word Embeddings

 

Week 8, 9, 10 - Advanced Applications
    Unsupervised Learning

        Autoencoders

    Recommendation Engines

    Reinforcement Learning
    Generative Adversarial Networks
    Internet Of Things (IOT)
        Rasberri Pi
        Arduino

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