SKU: 49757277854

Démanteler le Colonialisme Vert

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Démanteler le Colonialisme VertJustice nergtique et Climatique dans la Rgion Arabe La rgion arabe, carrefour gopolitique dot de rserves significatives d'nergies fossiles et marqu par un hritage colonial, recle un potentiel inexploit pour l'nergie verte. Pourtant, elle subit de plein fouet les impacts du changement climatique, comme en tmoignent les vagues de chaleur rcurrentes, les temptes de sable dvastatrices et les inondations catastrophiques. "Dismantling Green Colonialism:

Justice Énergétique et Climatique dans la Région Arabe

La région arabe, carrefour géopolitique doté de réserves significatives d'énergies fossiles et marqué par un héritage colonial, recèle un potentiel inexploité pour l'énergie verte. Pourtant, elle subit de plein fouet les impacts du changement climatique, comme en témoignent les vagues de chaleur récurrentes, les tempêtes de sable dévastatrices et les inondations catastrophiques. "Dismantling Green Colonialism: Energy and Climate Justice in the Arab Region”, un ouvrage provocateur co-publié avec Pluto Press en anglais, met en lumière la nécessité urgente d'une transition énergétique juste dans la région.

Ce dossier collaboratif se présente comme un manifeste appelant à une transition juste dans la région arabe — incluant la Palestine, la Jordanie, les Émirats Arabes Unis, l'Arabie Saoudite, le Soudan, l'Égypte, la Tunisie, l'Algérie, le Sahara Occidental et le Maroc. Il repose sur une analyse de classe et une approche d’économie politique, essentielles pour défier et démanteler les structures hégémoniques et oppressives ayant longtemps entravé les perspectives d'un avenir juste et durable pour la région.

Sur les auteur.es

Hamza Hamouchene est un chercheur et militant algérien basé à Londres, commentateur et membre fondateur de Algeria Solidarity Campaign (ASC), Environmental Justice North Africa (EJNA) et du Réseau nord-africain de souveraineté alimentaire (Siyada). Il est actuellement coordinateur du programme pour le monde arabe au Transnational Institute (TNI). Son travail se concentre sur les questions d'extractivisme, de ressources, de souveraineté foncière et alimentaire, ainsi que la justice climatique, environnementale et énergétique dans le monde arabe. Il a notamment écrit et édité trois ouvrages : Les soulèvements arabes, une décennie de luttes (2022), La lutte pour la démocratie énergétique au Maghreb (2017) et La prochaine révolution en Afrique du Nord : la lutte pour la justice climatique (2015).

Katie Sandwell est chargée de programme au Transnational Institute (TNI). Son travail porte notamment sur les questions de justice autour de l’accès à la terre et aux ressources, la souveraineté alimentaire et la transition juste. Katie est titulaire d'un diplôme de Master en études environnementales de l'Université de York, au Canada, avec une spécialisation sur la souveraineté alimentaire et les mouvements locaux. Elle a publié ou contribué à des publications sur les mouvements internationaux de souveraineté alimentaire, la transition juste et les réglementations en matière de drogues, notamment « De la Crise a la Transformation : Qu’est-ce une transition juste ? » (Transnational Institute, 2022) avec Kali Akuno, Lyda Fernanda Forero et Jaron Browne.

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4.7 ★★★★★
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Par
Charlottesville, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
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Verified Purchase
Richard Hackathorn
Charlottesville, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022
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Verified Purchase
Amazon Customer
Belleville, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
Fort Morgan, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026
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Verified Purchase
Tommy Jonsson
Natrona Heights, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026

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