Encoder-Decoder Architecture for Seq2Seq Models | LSTM-Based Seq2Seq Explained скачать в хорошем качестве

Encoder-Decoder Architecture for Seq2Seq Models | LSTM-Based Seq2Seq Explained

📌 Resources: This video is a part of my course: Modern AI: Applications and Overview https://courses.computing4all.com/cou... In this video, we discuss the Encoder-Decoder architecture for sequence-to-sequence (Seq2Seq) models, one of the foundational approaches for tasks like machine translation, text summarization, and chatbots. I'll guide you through how LSTM-based encoder-decoder models work, covering everything from the encoding and decoding process to the role of hidden and cell states in transferring context between the encoder and decoder. You'll also learn about the pros and cons of using LSTM-based encoder-decoder models, including benefits like stable training and limitations such as exposure bias. If you're interested in understanding Seq2Seq architectures and how they handle input and output sequences of varying lengths, this video is for you! What You’ll Learn: What is the Encoder-Decoder architecture? How LSTM-based Seq2Seq models work The role of hidden and cell states in passing context Advantages and limitations of encoder-decoder models Applications in machine translation, text generation, and more 📌 Subscribe for more AI, machine learning, and deep learning tutorials! -- Dr. Shahriar Hossain https://computing4all.com