12/10/2020
13/10/2020
MLOps for ML Engineers
Course outline
General concepts
- Tokenisation, parsing, …
- Embeddings and transfer learning
- Labeling data correctly
Deep Learning Architectures
- LSTM
- Contextualized String Embeddings using the ELMo model
- Attention mechanisms
- Transformer architecture
- BERT model
- BERT’s derivatives and when to use them
Case studies and exercises
- E-mail automation
- Document information extraction
- NLP for search
Teachers
Kaja Zupanc, PhD
Senior NLP Engineer
Data Scientist with a passion for NLP and a background as a researcher in Theoretical Linguistics. Specialized in formal approaches to typical aspects of linguistic interaction, such as question answering, implicit information, and discourse organization. Aleksandra loves tackling complex problems and finding patterns in unstructured data. A growing fascination for data science and its applications in Language Technology and AI led me to participate in a data science boot camp. I am an advocate of making science accessible to a broad audience and strongly believe that crowdsourcing is a crucial component of innovation.
Course level
expert
Admission Fee
€ 3000
Prerequisites
- Python programming at the intermediate level
- Knowledge of basic TensorFlow concepts (we use the Keras API)
- Knowledge of basic Machine Learning concepts like data splitting, classification, overfitting, probabilities, ...
You get
Course material
Drinks, snacks and lunch
Cloud servers for use during training
4h of support and question answering up to 6 months after the course
