About this course
Through predictive text, translation tools, and smart devices natural language processing (NLP) is increasingly a part of our day-to-day lives, and in large language models like Chat-GPT we see the enormous future potential of this exciting area of research. This advanced course examines the theoretical concepts of NLP and its current and potential future application in diverse domains.
The course begins with an introduction to attention mechanisms, examining self-attention, transformers, and byte pair encoding, before turning to large language models (LLMs) and natural language generation, exploring how they use prompting and reinforcement learning with human feedback. You will look closely at the varied applications of NLP and LLMs in particular, such as question answering, translation, and code generation. In the final part of the course you will discover how language and vision can interact in applications such as video captioning or text to image generation, before looking to the future of NLP research and considering the limitations, biases, ethical concerns, and potential misuses of NLP.
This intensive course offers students theoretical understanding and practical experience in a range of natural language processing concepts and techniques, offering career skills as well as excellent foundations for future research.
By the end of this course, you will:
- Be able to demonstrate understanding of the algorithms and methods used to process textual data.
- Understand the functionality of large language models and their training through finetuning, low-rank adaptation, and quantized low-rank adaptation.
- Demonstrate understanding of the practical applications of natural language processing.
- Be able to discuss the potential limitations, biases, ethical concerns, and misuses of NLP.
Who is this course suitable for?
This course would suit STEM students with intermediate level experience in artificial intelligence, machine learning, and natural language processing concepts and techniques, including those undertaking, or looking ahead to, graduate level study or research.
Specifically, students on this course must have experience of the following topics:
- Knowledge of the deep learning libraries.
- Understanding of deep learning, recurrent neural networks, GRU, and LSTMs.
- Strong background in optimization and probability.
- Familiarity with the Python programming language.
Dates and availability
Available as a Residential or Online course on the following date:
Session 2: 15th July to 2nd August 2024
Click below to find out how to apply.
Get in touch
If you have any questions, or would like to know more, please get in touch via the link below.