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Encoder-Decoder Architecture Training

Live Online & Classroom Enterprise Training

Learn the fundamentals of Encoder-Decoder architecture, a core deep learning design used in sequence-to-sequence tasks such as machine translation, text summarization, and speech recognition. This course covers concepts, working principles, implementation approaches, and real-world applications.

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What is Encoder-Decoder Architecture Course about?

The Encoder-Decoder architecture is widely used in modern Artificial Intelligence and Deep Learning systems for handling sequential data. In this course, learners will explore how encoders convert input data into meaningful representations and how decoders generate output sequences from these representations. The training includes theoretical foundations, neural network structures, attention mechanisms, and hands-on implementation concepts used in real-world AI applications such as NLP and speech processing.

What are the objectives of Encoder-Decoder Architecture Course ?

  • Understand the working principles of Encoder and Decoder components 
  • Learn sequence-to-sequence modeling techniques 
  • Explore attention mechanisms and transformer basics 
  • Understand real-world AI use cases like translation and summarization 
  • Gain knowledge of implementation workflows in deep learning frameworks 

Who is Encoder-Decoder Architecture Course for?

  • AI / Machine Learning Engineers 
  • Data Scientists and Data Analysts 
  • Software Developers working on AI-based applications 
  • NLP Engineers and Researchers 
  • Students interested in Deep Learning and Neural Networks

What are the prerequisites for Encoder-Decoder Architecture Course?

Prerequisites:  

  • Basic understanding of Python programming 
  • Fundamentals of Machine Learning concepts 
  • Knowledge of Neural Networks basics 
  • Basic mathematics (Linear Algebra and Probability) 
  • Familiarity with Deep Learning frameworks is helpful 


Learning Path:  

  • Foundations of Neural Networks 
  • Sequence Modeling and RNN / LSTM Concepts 
  • Encoder-Decoder Architecture Fundamentals 
  • Attention Mechanism and Transformers Introduction 
  • Real-world Use Case Implementation Concepts 


Related Courses: 

  • Natural Language Processing Fundamentals 
  • Deep Learning with Neural Networks 
  • Transformer Architecture and Applications 
  • Sequence-to-Sequence Learning Models

Available Training Modes

Live Online Training

1 Days

Course Outline Expand All

Expand All

  • Overview of sequence-to-sequence tasks
  • Understanding the need for encoder-decoder models
  • Detailed explanation of the encoder component
  • Detailed explanation of the decoder component
  • Role of attention mechanisms in enhancing model performance
  • Preparing datasets for training
  • Implementing training procedures using TensorFlow
  • Deploying trained models for inference

Who is the instructor for this training?

The trainer for this Encoder-Decoder Architecture Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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