In this post, we introduce the encoder decoder structure in some cases known as Sequence to Sequence (Seq2Seq) model. For a better understanding of the structure of this model, previous knowledge on…
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...
bidirectional RNNs or BRNNs, pull in future data to improve the accuracy of it. Returning to the example of “feeling under the weather”, a model based on a BRNN can better predict that the second word in that phrase is “under” if it knows that the last word...
comprising encoder and decoder layers, enable gen AI models to learn relationships and dependencies between words in a more flexible way compared with traditional machine and deep learning models. That’s because transformer networks are trained on huge swaths of the internet (for example, all traffi...
Transformer networks, comprising encoder and decoder layers, allow gen AI models to learn relationships and dependencies between words in a more flexible way compared with traditional machine and deep learning models. That’s because transformer networks are trained on huge swaths of the internet (for...
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...
The model consists of two parts: the encoder and the decoder. The encoder is a feedforward, fully connected neural network that transforms the input vector, containing the interactions for a specific user, into an n-dimensional variational distribution. This variational distribution is used to obtai...
简单来讲就是Encoder作为编码器,用来获取输入数据的feature或者context(context vector一般是padding成固定的长度),然后输入到Decoder中解码得到输出。Seq2Seq的输入和输出都可以是任意长度。在机器翻译模型中,Encoder和Decoder一般都是Recurrent Neural Network;在应用到图像领域中时,Encoder一般采用CNN获取图像的feature,再用...
Variational auto-encoders (VAEs): VAEs are generative models that learn the underlying structure of data and are commonly used for tasks like image generation. Autoregressive models: Autoregressive models predict the next value in a sequence based on previous values, commonly used in time series ...
A variational autoencoder (VAE) is a generative AI algorithm that uses deep learning to generate new content, detect anomalies and remove noise. VAEs first appeared in 2013, about the same time as other generative AI algorithms, such as generative adversarial networks (GANs) and diffusion models...