NMT and Speech Recognition Training in Copenhagen, Denmark

This course provides an in-depth understanding of Neural Machine Translation (NMT) and Speech Recognition techniques, covering deep learning models, NLP techniques, and real-world applications. Participants will gain hands-on experience in training, evaluating, and deploying NMT and speech recognition models.

This course covers:

• Fundamentals of Neural Machine Translation (NMT)

• Speech Recognition principles and techniques

• Deep Learning models for NLP and Speech Processing

• Tokenization, Embeddings, and Sequence-to-Sequence Models

• Transformer-based architectures (e.g., BERT, GPT, Whisper, Wav2Vec)

• Training and Fine-tuning Speech and Translation Models

• Real-world applications and use cases

Course Agenda

Introduction to Neural Machine Translation (NMT) & Speech Recognition

• Overview of NLP and Speech Processing

• Applications and real-world use cases

• Comparison of traditional vs. neural-based approaches

Fundamentals of Neural Machine Translation (NMT)

• Introduction to Sequence-to-Sequence (Seq2Seq) models

• Word embeddings and tokenization strategies

• Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTMs)

• Attention Mechanism and its impact on translation

Advanced NMT Architectures

• Transformer-based models

• Training large-scale translation models

• Fine-tuning pre-trained models for specific domains

Fundamentals of Speech Recognition

• Basics of Speech Processing

• Feature extraction

Training and Evaluating Speech Recognition Models

• Dataset preparation for speech recognition

• Handling noise and variations in speech

Deploying NMT and Speech Models

• Optimizing models for deployment

• Running speech and translation models on edge devices

• Building APIs for real-time translation and speech processing

Case Studies and Hands-on