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1D convolution for neural networks, part 1: Sliding dot product
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Classroom Contents
Convolution in One Dimension for Neural Networks
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- 1 1D convolution for neural networks, part 1: Sliding dot product
- 2 1D convolution for neural networks, part 2: Convolution copies the kernel
- 3 1D convolution for neural networks, part 3: Sliding dot product equations longhand
- 4 1D convolution for neural networks, part 4: Convolution equation
- 5 1D convolution for neural networks, part 5: Backpropagation
- 6 1D convolution for neural networks, part 6: Input gradient
- 7 1D convolution for neural networks, part 7: Weight gradient
- 8 1D convolution for neural networks, part 8: Padding
- 9 1D convolution for neural networks, part 9: Stride
- 10 Implement 1D convolution, part 1: Convolution in Python from scratch
- 11 Implement 1D convolution, part 2: Comparison with NumPy convolution()
- 12 Implement 1D convolution, part 3: Create the convolution block
- 13 Implement 1D convolution, part 4: Initialize the convolution block
- 14 Implement 1D convolution, part 5: Forward and backward pass
- 15 Implement 1D convolution, part 6: Multi-channel, multi-kernel convolutions
- 16 Implement 1D convolution, part 7: Weight gradient and input gradient
- 17 Build a 1D convolutional neural network, part 1: Create a test data set
- 18 Build a 1D convolutional neural network , part 2: Collect the Cottonwood blocks
- 19 Build a 1D convolutional neural network , part 3: Connect the blocks into a network structure
- 20 Build a 1D convolutional neural network, part 4: Training, evaluation, reporting
- 21 Build a 1D convolutional neural network, part 5: One Hot, Flatten, and Logging blocks
- 22 Build a 1D convolutional neural network, part 6: Text summary and loss history
- 23 Build a 1D convolutional neural network, part 7: Evaluate the model
