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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

Description:

Dive into the fundamentals of one-dimensional convolution for neural networks in this comprehensive video series. Explore the sliding dot product concept, convolution equations, backpropagation techniques, and practical implementations. Learn how to handle padding, stride, and multi-channel, multi-kernel convolutions. Progress through hands-on Python implementations, comparing custom solutions with NumPy functions. Culminate the learning experience by building a complete 1D convolutional neural network, covering everything from data set creation to model evaluation and performance reporting.

Convolution in One Dimension for Neural Networks

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#Computer Science #Artificial Intelligence #Neural Networks #Deep Learning #Data Science #Data Analysis #Time Series Analysis #Data Preprocessing #Machine Learning #Model Evaluation #Backpropagation

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