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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  • LeNet-5
  • AlexNet
  • VGG-16

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  1. Convolutional Neural Networks
  2. Deep Convolutional Models: Case Studies

Classic Networks

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Last updated 4 years ago

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LeNet-5

  • It was developed by Yann LeCun et al. in 1998

  • The goal of this network was to classify handwritten digits

  • It was trained using 32x32x1 grayscale images

  • This net is smaller compared to today's standards; it had about 60K parameters

  • It is interesting to note that as the height and width of the image decreased across the layers, the number of channels increased

  • Also, the net had a sigmoid non-linearity after the pooling layers, which is no longer used today after pooling

AlexNet

  • AlexNet was developed by Alex Krizhevsky, Geoffrey Hinton, and Ilya Sutskever in 2012

  • It is much bigger than LeNet-5 and has about 60M parameters

VGG-16

  • It was developed by K. Simonyan and A. Zisserman in 2014

  • Instead of having thousands and thousands of parameters, this model used fixed parameters

    • All its convolutional layers had 3x3 filters with stride 1 and "same" padding

    • All its max-pooling layers were 2x2 and had stride 2

  • It had a very simplified architecture: