Deep Learning Convolutional Neural Networks Preliminaries

Deep Learning Convolutional Neural Networks Preliminaries
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In this study, the focus is on the preliminaries of deep learning convolutional neural networks, transitioning from fully connected layers to convolutional layers for image processing. Various concepts such as padding, stride, multiple input/output channels, and pooling are explored with LeNet architecture. Additionally, topics like translation invariance, locality, kernel customization, and different types of pooling methods including maximum and average pooling are covered in detail.

  • Deep Learning
  • Convolutional Neural Networks
  • Image Processing
  • LeNet
  • Preliminaries
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  1. Dive into Deep Learning Convolutional Neural Networks Preliminaries / JunHo Yoon From FC Layers to Convolutions Convolutions for Images Padding and Stride Multiple(I/O) Channels Pooling LeNet

  2. Dive into Deep Learning Preliminaries * CNN From FC Layers to Convolutions Convolutions for Images Padding and Stride Multiple(I/O) Channels Pooling LeNet Translation Invariance : Loclity :

  3. Dive into Deep Learning Preliminaries * Convolution Layer From FC Layers to Convolutions Convolutions for Images Padding and Stride Multiple(I/O) Channels Pooling LeNet *

  4. Dive into Deep Learning Preliminaries padding : Input Convolution Layer stride : kernel 1/stride resize * default : padding = 0, stride = 1 From FC Layers to Convolutions Convolutions for Images Padding and Stride Multiple(I/O) Channels Pooling LeNet padding(W,H) = (kernel(W,H) 1) / 2

  5. Dive into Deep Learning Preliminaries kernel * Input kernel channel From FC Layers to Convolutions Convolutions for Images Padding and Stride Multiple(I/O) Channels Pooling LeNet * Output channel

  6. Dive into Deep Learning Preliminaries Maximum Pooling : Average Pooling : - - padding & strid Input & Output channel overfitting From FC Layers to Convolutions Convolutions for Images Padding and Stride Multiple(I/O) Channels Pooling LeNet * stride(W,H) = pooling(W,H)

  7. Dive into Deep Learning Preliminaries * LeNet Architecture From FC Layers to Convolutions Convolutions for Images Padding and Stride Multiple(I/O) Channels Pooling LeNet

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