Deep Learning Specialization - Coursera
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  • Introduction
  • Neural Networks and Deep Learning
    • Introduction to Deep Learning
    • Logistic Regression as a Neural Network (Neural Network Basics)
    • Shallow Neural Network
    • Deep Neural Network
  • Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
    • Practical Aspects of Deep Learning
    • Optimization Algorithms
    • Hyperparameter Tuning, Batch Normalization and Programming Frameworks
  • Structuring Machine Learning Projects
    • Introduction to ML Strategy
    • Setting Up Your Goal
    • Comparing to Human-Level Performance
    • Error Analysis
    • Mismatched Training and Dev/Test Set
    • Learning from Multiple Tasks
    • End-to-End Deep Learning
  • Convolutional Neural Networks
    • Foundations of Convolutional Neural Networks
    • Deep Convolutional Models: Case Studies
      • Classic Networks
      • ResNets
      • Inception
    • Advice for Using CNNs
    • Object Detection
      • Object Localization
      • Landmark Detection
      • Sliding Window Detection
      • The YOLO Algorithm
      • Intersection over Union
      • Non-Max Suppression
      • Anchor Boxes
      • Region Proposals
    • Face Recognition
      • One-Shot Learning
      • Siamese Network
      • Face Recognition as Binary Classification
    • Neural Style Transfer
  • Sequence Models
    • Recurrent Neural Networks
      • RNN Structure
      • Types of RNNs
      • Language Modeling
      • Vanishing Gradient Problem in RNNs
      • Gated Recurrent Units (GRUs)
      • Long Short-Term Memory Network (LSTM)
      • Bidirectional RNNs
    • Natural Language Processing & Word Embeddings
      • Introduction to Word Embeddings
      • Learning Word Embeddings: Word2Vec and GloVe
      • Applications using Word Embeddings
      • De-Biasing Word Embeddings
    • Sequence Models & Attention Mechanisms
      • Sequence to Sequence Architectures
        • Basic Models
        • Beam Search
        • Bleu Score
        • Attention Model
      • Speech Recognition
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  1. Sequence Models
  2. Recurrent Neural Networks

RNN Structure

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Last updated 4 years ago

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RNN Structure

The image below shows the structure of a simple RNN.

An RNN maps every x<i>x^{\lt i\gt}x<i> to a y<i>y^{\lt i\gt}y<i>, and while doing so, it uses information learned at previous timestamps. However, it does not use information from future timestamps.

a<t>=g(Waaa<t−1>+Wazx<t>+ba)a^{<t>} = g(W_{aa}a^{<t-1>}+W_{az}x^{<t>}+b_{a})a<t>=g(Waa​a<t−1>+Waz​x<t>+ba​)

y^<t>=g(Wyaa<t>+by)\hat{y}^{<t>}=g(W_{ya}a^{<t>}+b_{y})y^​<t>=g(Wya​a<t>+by​)