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self-attention
in self-attention the key, values and queries are all generated form the same sequence. (think of self-attention like cars in one city traveling along multiple roadways.)
cross-Attention will share information between sequences. (Channels are present and information is funneled by the assigned similarity)
channel
an individual information path.
(think of a channel like a lane the road)
channel attention uses this channel to funnel he information that is similar.
soft attention is when the information is placed on multiple channels rather then reading obtaining the information from each source the information is distributed (This is like same you have a cold so you place a box of tissues in every room in your house. so you don't need to go to a specific room to get the tissue)
spatial attention
special attention uses a concentrated approach to find the information that is most important and places that information in a specific space within the embedding to be referred to. (an example of this in the real world would be if you placed you keys on your coffee table every day when you get home, they would be their when you go to leave. This information has a specific place it's held based on its importance. You wouldn't place your towel paper on the coffee table, unless you used it to blow your nose. If a tissue box was introduced into the equation the towel paper would then have assigned to a new position and the tissue box would then be position on the coffee table. The keys are still there unless the car keys position needed to be changed based on importance of its placement in the space usually due to functionality. )
Embedding an embedding table.
Embedding weights, the information based on similarity and bias.
(Think of embedding like parking lot what only except s a certain type of Transportaion type, or the bike rack that sits on the side of the parking lot) The cars are embedded in the parking spots, the trucks may be parked along the longer stretches, bikes on the rack. although the size may or may not matter the similarity or likeness of the vehicle is the detail in question. The embedding is the organization of the information.) This only deals with the similarities of the information. based on its similarity of usage.
next step after embedding.
Position embedding
Positional embedding retains the structure of the information so that it does not become jumbled. " I fed my cat and walked my dog to the park."
without position embedding the computer may perceive the information as "I fed my dog to my cat and walked the park". All the same words are present, but the positions of the worlds have been changes because the computer has not been notified the importance of the word's positions.
Loss
difference between values that are observed and values that are predicted.
If there is Zero difference the loss is zero and the prediction is accurate.
observation: by observation is can be assumed that a perfect zero in loss would only reproduce the given observation I replication.
Welcome to the Workprint Studios Blog.
Attention is a technique in artificial intelligence (AI) systems that enables the system to focus on certain parts of the input data that are most relevant to the given task. In simple terms, attention allows the AI system to selectively process and give more weight to certain parts of the input while ignoring the rest. This technique has become a cornerstone in various deep learning models, including natural language processing, computer vision, and speech recognition. For example, in language translation, an attention-based model can focus on the most relevant parts of the source text while generating the target language sentence, resulting in more accurate translations.
Self-attention is a type of attention mechanism in which the key, values, and queries are generated from the same sequence. This allows the system to focus on specific areas within the input sequence, much like cars traveling along multiple roadways within a city. Self-attention is used extensively in natural language processing (NLP) tasks, allowing for improved sentence embedding and context-based language understanding.
Cross-attention, on the other hand, involves the sharing of information between sequences. Channels are present in cross-attention, and information is funneled through the assigned similarity. This allows the system to learn relationships between different inputs and focus on the most relevant information.
Channel attention uses individual information paths, known as channels, to funnel information that is similar. This allows the system to focus on relevant information and avoid processing unnecessary data. Channel attention is used extensively in image recognition tasks, allowing for improved object detection and classification.
Soft attention involves placing information on multiple channels, rather than obtaining the information from each source. This is similar to placing a box of tissues in every room of a house, making it easy to access the tissues without needing to go to a specific room. Soft attention is useful in tasks that involve multiple sources of input, such as machine translation.
Spatial attention involves a concentrated approach to finding the most important information and placing it in a specific space within the embedding to be referred to later. This is similar to placing keys on a coffee table every day, ensuring they are easily accessible when needed. Spatial attention allows the system to focus on important information and avoid processing unnecessary data.
Embedding involves weighting information based on similarity and bias. This allows the system to organize the data in a structured manner, similar to how cars are parked in a parking lot based on their size and shape. Embedding is used extensively in NLP tasks, allowing for improved sentence embedding and context-based language understanding.
Positional embedding is used to retain the structure of information, ensuring it does not become jumbled. This is similar to notifying the computer of the importance of word positions in a sentence to avoid misinterpretation. Positional embedding is useful in tasks that involve the processing of large amounts of data, ensuring the information is correctly interpreted by the system.
Loss is the difference between observed and predicted values. If there is zero difference, the loss is zero, and the prediction is accurate. Loss is used in the training of AI systems, allowing for the correction of errors and the improvement of system accuracy.
Attention mechanisms in AI systems allow for the focusing of the system's processing power on specific areas of input, resulting in more accurate predictions and improved performance. Self-attention, cross-attention, channel attention, soft attention, spatial attention, embedding, positional embedding, and loss are all essential components of attention mechanisms in AI systems. By utilizing these techniques, AI systems can perform complex tasks that were once reserved for humans. As AI technology continues to advance, attention mechanisms will play an increasingly vital role in improving system accuracy and performance.
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