Congratulations - you have successfully navigated to the exercise page for the book Deep Learning for Physics Research
This page hosts additional descriptions as well (where appropriate) example solutions to the exercise problems. While the solutions are available, we recommend you first attempt to solve the problem yourself, before looking up the answer. As many problems are open-ended, the presented solution of course often only represents one possible option how the problem could be solved.
Some helpful links:
Python Tutorial (https://docs.python.org/3.7/tutorial/index.html): an introduction to the Python programming language
Google Colab (https://colab.research.google.com/): for Python development in your web-browser
Anaconda (https://www.anaconda.com/products/individual): a Python distribution for local installation
numpy (https://numpy.org/doc/stable/user/quickstart.html): a widely used library for mathematical operations in Python
Keras (https://keras.io/): a beginner-friendly deep learning library used in these exercises
Tensor Flow (https://www.tensorflow.org/): a useful backend for deep learning development
SciKit Learn (https://scikit-learn.org/stable/): helpful machine learning library
Seaborn (https://seaborn.pydata.org/): a library for creating nice looking graphs and figures