Week 3: Why is this book necessary?
After studying degrowth and ecological economics for nearly 10 years, I’ve found that most of the literature focuses on:
1. Explaining why capitalism is unjust, dangerous, and at the root of multiple crises.
2. Highlighting alternatives that are more democratic and just, even if they aren’t nation-wide systems.
Regarding
(1), much of the focus is on averages, often portraying a class-free
world where some places are rich, and others are poor, neglecting
distribution differences within countries, class disparities, and the
internationalization of capital. The Global North and South narrative,
while useful for discussing structural inequalities, falls short in
fostering a global working-class consciousness. We must address colonial
arrangements and engage the working class across the globe.
Regarding
(2), it’s essential to showcase "nowtopias"—places where time is
abundant, poverty is absent, and coexistence with other living beings
has thrived for millennia. However, the critical question remains: how
do we create our own utopia, and how do we navigate toward it? We need
theories of transformation and tools for transition to design, organize,
and implement democratic alternatives within planetary boundaries.
I’m building upon three key books:
- Degrowth and Strategy: How to Bring about Social-Ecological Transformation(2020)
- Deep Transformations: A Theory of Degrowth (2024)
- Towards a Political Economy of Degrowth(2019)
These books help, but we also need:
- A toolbox to understand what has worked historically to make transitions mainstream.
- A theory of change that centers on class as a key transformation element.
- Alternatives embedded in a new economic system rooted in human and nature rights.
This
book will help you design, organize, and implement the future you
envision. We all know a just, free, and beautiful world is possible; we
need to believe in it again and relearn the art and science of
transition-making. This book provides tentative answers to these
questions, which is why it’s important to bring it to light.
If
you, like the many who already support this project, believe in its
importance, please support its publication in multiple languages with
the highest sustainability standards.
Thanks to Diana Kobus, Eva, Juanjo, Stella Martinez McShera, Oscar, Ivan Golenko, Ivan Golenko, and many others for your support. Your energy keeps me going.
With love and solidarity. Please support if you care.
FastAI Deep Learning Journey Part 7: Calculating crowd size using image regression, a potential application for train use
In the previous post we show how to use a more general approach for the case when images may have one, multiple or any label at all. In this post, we will show how very little changes are required to implement computer vision deep learning methods for regression problems. To make things less theoretical, we picked a very interesting data set, containing 2000 images from people in a shooping mall. Each picture has been carefully labeled, where we can find between 12 to 60 people. This could be a very interesting application for example for public transport usage, as the extension of monthly tickets may very hard to track the usage of each train/bus or other service in real life. We will show that with only 3 epochs /1GPU we managed to get ~2 MAE (mean absolute error) or in order words, get the counting wrong 2+/- person, which is really not a lot considering we can have 60 people in the image. Let see in detail what needs to be changed and explain a potential usage for public transport ...

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