Coding exercises, slides and datasets used in the 2020 course Introduction to deep learning for biologists.
Exercises are in the form of jupyter notebooks and can be either downloaded and run locally or explored online via platforms such as Binder, Google Colab (a free Google account is required) or Kaggle Notebooks (a free Kaggle account plus phone verification is required).
These exercises showcase some basic Python concepts, plus the use of most common modules.
- Balance: solving a simple physics problem
- Topics: lists, loops, functions, execution times and plots
- Words: word count and other text statistics
- Topics: lists, dictionaries, Pandas dataframes, zip archives, remote and local files, string manipulation
- Image: image manipulation
- Topics: numpy arrays, matplotlib pyplot, Pillow (PIL) and images in general, plus remote and local saving
- Deck: implementing a deck of cards
- Topics: python classes, attributes, methods, inheritance
Here you find all the slides used in the course.
All codes used during the lessons plus some extra exercises (with solutions):