Feature Learning with Convolutions and Embeddings

A fixed or learned vector representation of each individual token in your input sequence, like a word.

A matrix that stores all the possible token embeddings. Each row corresponds to a specific token from the vocabulary, and each column represents a dimension of the embedding.

The size of the vocabulary, which is the total number of unique tokens in your dataset.

The dimension of each token embedding, meaning the number of features used to represent the token. This can be fixed or learned during training.

The process of converting a sequence of tokens into a sequence of token embeddings by retrieving them from the embedding matrix.

A technique to combine multiple token embeddings into a single vector representation for the entire sequence. This is used to summarize the information from all the tokens.

A way to perform sequence aggregation by taking the average of all the token embeddings in the sequence. It treats all tokens equally.

A way to perform sequence aggregation by taking the maximum value for each dimension across all token embeddings in the sequence. It focuses on the most important features.

A process in deep learning where a neural network learns its own features for structured data, rather than relying on manually crafted features.

A type of linear layer in a deep network that can be applied to inputs of varying sizes by sliding over the input data and performing dot products.

The process of converting discrete data, such as words in a sentence, into a continuous representation that neural networks can understand.

A filter used in convolutions that performs averaging over a specified window size. It smoothes out the input by averaging neighboring values.

A filter used in convolutions that highlights specific patterns. It can detect peaks, edges, or other features in the input.

A filter used in convolutions that identifies specific values or ranges of values in the input. This allows the network to focus on particular components of the data.

The act of adding zero values (padding) to the edges of an input signal before applying a convolution. This ensures the output of the convolution has the same size as the original input.

The process of adding dimensions to the output of convolutional layers by applying multiple filters in parallel. This allows the network to learn multiple features simultaneously.

A convolution layer in neural networks is a layer that applies a filter to the input data. The filter is like a small window that slides over the input and performs a dot product at each position. This process extracts features from the data and generates a new representation.

Max pooling is a technique used in convolutional neural networks to reduce the spatial dimensions of the feature maps. It selects the maximum value from a small region of the input, effectively downsampling the data.

Stacking convolution layers with max pooling creates a hierarchy of feature representations. Initial layers extract fine-grained features, while later layers capture more abstract and global patterns. This process is analogous to how our visual system processes information from simple edges to complex objects.

2-D convolutions are specifically designed for grid-like data, such as images. They use square-shaped filters that slide over the input, extracting features in both horizontal and vertical directions. This technique captures spatial relationships in the data.

Striding refers to the step size of the filter when it moves across the input data. A stride of 1 means the filter moves one position at a time, while a larger stride skips positions. Striding can improve computation efficiency and reduce redundancy.

Convolution works best when the patterns to detect are localized. Deeper convolution layers capture more complex and global patterns, while shallower layers focus on local features. The choice of layer depth depends on the complexity of the patterns in the data.

Transpose convolution is a type of convolutional operation that upsamples the data by increasing its dimensions. It is often used to reconstruct the original resolution of the data after downsampling through pooling operations.

U-Net is a convolutional neural network architecture commonly used for semantic image segmentation. It uses a combination of downsampling and upsampling operations to accurately segment objects within images.