Why do you want to preprocess and review data before using it to train a machine learning model?
この演習はコースの一部です
理論を実践に変える、インタラクティブな演習のひとつをお試しください
In this module, you will explore the power of language models and their real-world applications. Starting with a manual method for modelling language, you will investigate the role that probabilities and randomness play in next word prediction. You will also consider the course learning objectives and how to most effectively study.
In this module, you will move beyond the manual method and explore how n-grams can be used to tokenize data. You will investigate how probabilities can be calculated to begin identifying language patterns. You will then build your own n-gram model using a small dataset and examine its limitations. Furthermore, you will consider the process researchers undertake when approaching real-world problems through the lens of Google DeepMind’s AlphaFold project. Finally, you will reflect on your own values and those of your community, as well as the role AI systems play in making decisions that involve ethical choices.
In this module, you will experiment with more sophisticated transformer models and evaluate how they perform in comparison to n-gram models. You will take a deeper dive into the anatomy of language models and their core components. You will continue reflecting on the role that values play in guiding which technical problems you choose to solve. Specifically, you will consider the Ubuntu moral system and compare its characteristics with moral values popular in Europe and North America. Finally, you will design a values framework for guiding LLM development in your local community.
In this module, you will contextualise the process of building language models within the machine learning development pipeline. You will preprocess your dataset and learn how to prepare a dataset to be used for training a transformer model. You will then train your own language model and evaluate its performance.
現在の演習
In this module, you will consider the specific benefits that transformer LLMs can bring about for different sectors in your local context. You will then explore what makes a good problem statement before developing your own problem statement for a challenge around language models that you have identified in your community.
In this module, you will have the opportunity to consult additional resources and further reading to investigate the topics you have covered in more detail. Finally, you will consider your next steps and how you can build on what you have learned in the course.
