Categories: AI/ML Research

Building a Transformer Model for Language Translation

This post is divided into six parts; they are: • Why Transformer is Better than Seq2Seq • Data Preparation and Tokenization • Design of a Transformer Model • Building the Transformer Model • Causal Mask and Padding Mask • Training and Evaluation Traditional seq2seq models with recurrent neural networks have two main limitations: • Sequential processing prevents parallelization • Limited ability to capture long-term dependencies since hidden states are overwritten whenever an element is processed The Transformer architecture, introduced in the 2017 paper “Attention is All You Need”, overcomes these limitations.
AI Generated Robotic Content

Recent Posts

VHS filters work great with AI footage (WAN 2.2 + NTSC-RS)

submitted by /u/mtrx3 [link] [comments]

22 hours ago

Algorithm Showdown: Logistic Regression vs. Random Forest vs. XGBoost on Imbalanced Data

Imbalanced datasets are a common challenge in machine learning.

22 hours ago

Unlock global AI inference scalability using new global cross-Region inference on Amazon Bedrock with Anthropic’s Claude Sonnet 4.5

Organizations are increasingly integrating generative AI capabilities into their applications to enhance customer experiences, streamline…

22 hours ago

Connect Spark data pipelines to Gemini and other AI models with Dataproc ML library

Many data science teams rely on Apache Spark running on Dataproc managed clusters for powerful,…

22 hours ago

The Lenovo Go S Is $120 Off

The upgraded version of the Legion Go S with SteamOS makes for a nice Steam…

23 hours ago

AI could make it easier to create bioweapons that bypass current security protocols

Artificial intelligence is transforming biology and medicine by accelerating the discovery of new drugs and…

23 hours ago