First, the encoder compresses the input data into a more efficient representation. Encoders generally consist of multiple layers with fewer nodes in each layer. As the data is processed through each layer, the
Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain. Some form of deep learning powers most of the artificial intelligence (AI) applications in our lives today. The chi...
Prior to rebuilding the input in the decoder, a contractive autoencoder funnels it through a bottleneck. The bottleneck function is being used to learn an image representation of the image while it is being processed. The contractive autoencoder additionally has a regularization term to prevent the...
Deep learning is a subset ofmachine learningthat uses multilayeredneural networks, called deep neural networks, to simulate the complex decision-making power of the human brain. Some form of deep learning powers most of theartificial intelligence (AI)applications in our lives today. The chief diffe...
Encoder only: These models are typically suited for tasks that can understand language, such as classification and sentiment analysis. Examples of encoder-only models include BERT (Bidirectional Encoder Representations from Transformers). Decoder only: This class of models is extremely good at generating...
A transformer architecture consists of an encoder and decoder that work together. The attention mechanism lets transformers encode the meaning of words based on the estimated importance of other words or tokens. This enables transformers to process all words or tokens in parallel for faster performance...
to understand relevant attributes of the target, such as facial expressions and body language. It then imposes these characteristics onto the original video. This autoencoder includes an encoder, which encodes the relevant attributes and a decoder, which imposes these attributes onto the target video...
This is achieved through the self-attention mechanism, a layer that is incorporated in both the encoder and the decoder. The goal of the attention layer is to capture the contextual relationships existing between different words in the input sentence. Nowadays, there are many versions of pre-...
So, what is generative AI? How does it work? And most importantly, how can it help you in your personal and professional endeavors? This guide takes a deep dive into the world of generative AI. We cover different generative AI models, common and useful AI tools, use cases, and the adva...
The entire mechanism is spread across 2 major layers of encoder and decoder. Some models are only powered with a pre-trained encoder, like BERT, which works with doubled efficiency. A full-stacked transformer architecture contains six encoder layers and six decoder layers. This is what it looks...