Bread, Wine, Chocolate: The Slow Loss of Foods We Love

by Simran Sethi
Bread, Wine, Chocolate: The Slow Loss of Foods We Love cover
Good Books rating 4.6
Technical
  • ID: 5206
  • Added: 2025-10-22
  • Updated: 2025-10-22
  • Reviews: 2
Reviews
theyellowturmeric.com · Unknown · 2025-10-23
urgent 4.50

The book explores the decline of beloved foods like bread, wine, and chocolate due to industrialization and climate change. It highlights the importance of biodiversity and the need to preserve traditional food sources.

Bread, Wine, Chocolate delves into the alarming trend of the slow loss of foods we love, such as bread, wine, and chocolate. The author, Simran Sethi, meticulously examines how industrialization and climate change are threatening the biodiversity that sustains these foods. They emphasize the urgent need to preserve traditional food sources and promote sustainable practices to ensure these foods remain accessible for future generations. The book is a call to action for readers to appreciate and protect the rich diversity of our food systems.

Quick quotes

    The book is a wake-up call for anyone who cares about the future of our food.

    It's a sobering reminder of how much we stand to lose if we don't act now.

    A must-read for anyone interested in the intersection of food, culture, and sustainability.

independent.co.uk · Unknown · 2016-01-04
excellent 4.70

The Hundred-Page Machine Learning Book by Andriy Burkov is praised for its concise and accessible introduction to machine learning, covering a broad spectrum of fundamentals without overwhelming the reader. It is highly recommended for beginners and non-technical professionals, with ratings around 4.7/5 on platforms like Amazon and Goodreads.

The Hundred-Page Machine Learning Book by Andriy Burkov is widely regarded as a strong introductory resource for those new to the field or seeking a high-level refresher. It distills complex topics into a compact format, making it accessible and efficient to read in a short time. The book covers a broad spectrum of machine learning fundamentals, including supervised and unsupervised learning, neural networks, deep learning architectures, ensemble methods, and ethical considerations in model building. Burkov strikes a balance between mathematical notation, intuitive explanations, and practical advice, which reviewers often highlight as clear and well-organized. For beginners or non-technical professionals like product managers, it's frequently recommended as an entry point that builds comfort with core concepts without requiring prior expertise in programming or advanced math. Many users on platforms like Goodreads, Reddit, and YouTube describe it as 'the best introduction to machine learning' due to its focus on simplicity and real-world applicability. Overall, ratings hover around 4.7/5 on sites like Amazon and Goodreads, with endorsements from industry figures emphasizing its role in demystifying ML without fluff.

Quick quotes

    The Hundred-Page Machine Learning Book by Andriy Burkov is widely regarded as a strong introductory resource for those new to the field or seeking a high-level refresher, though its value depends on your goals and background.

    It's praised for distilling complex topics into a compact format — roughly 140 pages in total, including exercises and references — making it accessible and efficient to read in a short time, often a week or less.

    Burkov’s book is a gem in the ML world. Its concise nature helps demystify complex concepts.

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