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    Quick Facts

    Medium Of InstructionsMode Of LearningMode Of Delivery
    EnglishSelf StudyVideo and Text Based

    Courses and Certificate Fees

    Certificate Availability
    no

    The Syllabus

    • Guided entry for students who have not taken the first course in the series
    • Notational conventions
    • Basic ideas: linear regression, classification
    • Recipe for Machine Learning

    • Neural Networks Overview
    • Coding Neural Networks: Tensorflow, Keras
    • Practical Colab

    • A neural network is a Universal Function Approximator
    • Convolutional Neural Networks (CNN): Introduction
    • CNN: Multiple input/output features
    • CNN: Space and time

    • Recurrent Neural Networks (RNN): Introduction
    • RNN Overview
    • Generating text with an RNN

    • Back propagation
    • Vanishing and exploding gradients
    • Initializing and maintaining weights
    • Improving trainability
    • How big should my Neural Network be ?

    • Interpretation: Preview
    • Transfer Learning
    • Tensors, Matrix Gradients

    • Gradients of an RNN
    • RNN Gradients that vanish and explode
    • Residual connections
    • Neural Programming
    • LSTM
    • Attention: introduction

    • Neural Language Processing (NLP)
    • Interpretation: what is going on inside a Neural Network
    • Attention
    • Adversarial examples
    • Final words

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