1D convolution for neural networks, part 1: Sliding dot product

1D convolution for neural networks, part 2: Convolution copies the kernel

1D convolution for neural networks, part 3: Sliding dot product equations longhand

1D convolution for neural networks, part 4: Convolution equation

1D convolution for neural networks, part 5: Backpropagation

1D convolution for neural networks, part 6: Input gradient

1D convolution for neural networks, part 7: Weight gradient

1D convolution for neural networks, part 8: Padding

1D convolution for neural networks, part 9: Stride

Implement 1D convolution, part 1: Convolution in Python from scratch

Implement 1D convolution, part 2: Comparison with NumPy convolution()

Implement 1D convolution, part 3: Create the convolution block

Implement 1D convolution, part 4: Initialize the convolution block

Implement 1D convolution, part 5: Forward and backward pass

Implement 1D convolution, part 6: Multi-channel, multi-kernel convolutions

Implement 1D convolution, part 7: Weight gradient and input gradient

Build a 1D convolutional neural network, part 1: Create a test data set

Build a 1D convolutional neural network , part 2: Collect the Cottonwood blocks

Build a 1D convolutional neural network , part 3: Connect the blocks into a network structure

Build a 1D convolutional neural network, part 4: Training, evaluation, reporting

Build a 1D convolutional neural network, part 5: One Hot, Flatten, and Logging blocks

Build a 1D convolutional neural network, part 6: Text summary and loss history

Build a 1D convolutional neural network, part 7: Evaluate the model