Mlce Book 01 Introduction Python Ipynb At Main Github

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This repo contains the building material for a JupyterBook which is intended to serve as a template/prototype for the hands-on part of a Machine Learning in Chemical Engineering (MLCE) course. This was a collective effort between the Process Systems Engineering group at the Otto von Guericke University / MPI Magdeburg and the Optimisation and Machine Learning for Process Systems Engineering group at Imperial College... The book aims at covering application case-studies in chemical engineering of If you have nice tutorials in the areas mentioned above reflecting case-studies in chemical engineering, we encourage you to share it with the community! 💪 For practical reasons, it is better if you submit your pull-request including a link to a working Colab Notebook. Let us know!

Submit your issue here and we will fix it. We encourage you to contribute to this resource! There was an error while loading. Please reload this page. There was an error while loading. Please reload this page.

This Notebook was originally prepared by Mathieu Blondel and few modifications have been made by us. We will learn about the programming language Python as well as NumPy and Matplotlib, two fundamental tools for data science and machine learning in Python. Notebooks are a great way to mix executable code with rich contents (HTML, images, equations written in LaTeX). Google Colab allows to run notebooks on the cloud for free without any prior installation, while leveraging the power of GPUs. This document that you are reading is not a static web page, but an interactive environment called a notebook, that lets you write and execute code. Notebooks consist of so-called code cells, blocks of one or more Python instructions.

For example, here is a code cell that stores the result of a computation (the number of seconds in a day) in a variable and prints its value: Click on the “play” button to execute the cell. You should be able to see the result. Alternatively, you can also execute the cell by pressing Ctrl + Enter if you are on Windows/Linux or Command + Enter if you are on a Mac. This book is intended to serve as a template/prototype for the hands-on part of a Machine Learning in Chemical Engineering (MLCE) course. This was a collective effort between the Process Systems Engineering group at the Otto von Guericke University / MPI Magdeburg and the Optimisation and Machine Learning for Process Systems Engineering group at Imperial College...

This book was prepared by Edgar Ivan Sanchez Medina, Antonio del Rio Chanona and Caroline Ganzer with the help of many collegues. If you have a question or need some help in adapting this book to your MLCE course feel free to contact us! Sanchez Medina, Edgar Ivan; del Rio Chanona, Ehecatl Antonio; and Ganzer, Caroline. (2023). Machine Learning in Chemical Engineering (v1.0.0). Zenodo.

https://doi.org/10.5281/zenodo.7986905 We are also very grateful to the JupyterBook project which allows this materials to be developed in a relatively simple way! There was an error while loading. Please reload this page.

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