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

Inception

The Inception model was introduced by researchers at Google in 2014.

It made use of 1x1 convolutions that are used to increase or shrink the number of channels without affecting the height and width of the image.

The concept of 1x1 convolutions was introduced in the paper for the Network in Network model.

A single inception block allows the network to use a combination of 1x1, 3x3, 5x5 convolutions and pooling. Inception blocks usually use 1x1 convolutions to reduce the input data volume’s size before applying 3x3 and 5x5 convolutions.

The Inception model consists of multiple such inception blocks.S

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Last updated 4 years ago

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