Mar 10, 2021 · There are three main blocks in the encoder-decoder model, The Encoder will convert the input sequence into a single-dimensional vector (hidden vector). The decoder will convert the hidden...

Feb 27, 2025 · In this article we will explore the architecture and working of transformers by understanding their key components, mathematical formulations and how they function during training and inference.

Jun 24, 2023 · Using an encoder-decoder architecture, the model can take an input image and generate a caption that accurately describes the contents of the image. This is achieved by first encoding each...

Feb 15, 2025 · The encoder-decoder architecture is a fundamental framework in deep learning, commonly used in tasks such as sequence-to-sequence modeling, machine translation, and image generation.

Over 200 figures and diagrams of the most popular deep learning architectures and layers FREE TO USE in your blog posts, slides, presentations, or papers.

Mar 11, 2021 · In this article, I aim to explain the encoder-decoder sequence-to-sequence models in detail and help build your intuition behind its working. For this, I have taken a step-by-step...

Encoder-decoder architectures can handle inputs and outputs that both consist of variable-length sequences and thus are suitable for sequence-to-sequence problems such as machine translation. The encoder takes a variable-length sequence as input and transforms it into a state with a fixed shape.

The encoder-decoder classifier architecture represents a sophisticated approach to machine learning that combines data compression and classification capabilities. This powerful architecture enables both efficient data representation and accurate classification tasks, making it particularly valuable for complex data processing scenarios.

Jan 12, 2024 · In the field of AI / machine learning, the encoder-decoder architecture is a widely-used framework for developing neural networks that can perform natural language processing (NLP) tasks such as language translation, text summarization, and question-answering systems, etc which require sequence-to-sequence modeling.

Mar 29, 2025 · Transformer is a deep learning architecture that is very popular in natural language processing (NLP) tasks. It is a type of neural network that is designed to process sequential data, such as text. In this article, we will explore the concept of attention and the transformer architecture. Specifically, you will learn: What problems …