Here Is a Preview of What You'll Learn In This Book…
- Convolutional neural networks structure
- How convolutional neural networks actually work
- Convolutional neural networks applications
- The importance of convolution operator
- Different convolutional neural networks layers and their importance
- Arrangement of spatial parameters
- How and when to use stride and zero-padding
- Method of parameter sharing
- Matrix multiplication and its importance
- Pooling and dense layers
- Introducing non-linearity relu activation function
- How to train your convolutional neural network models using backpropagation
- How and why to apply dropout
- CNN model training process
- How to build a convolutional neural network
- Generating predictions and calculating loss functions
- How to train and evaluate your MNIST classifier
- How to build a simple image classification CNN
- And much, much more!
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