glp 1 safe Safety and Tolerability of Glucagon-Like Peptide-1 Receptor Agonists: A State-of-the-Art Narrative Review | Drugs

glp 1 safe Safety and Tolerability of Glucagon-Like Peptide-1 Receptor Agonists: A State-of-the-Art Narrative Review | Drugs

Name: glp 1 safe Safety and Tolerability of Glucagon-Like Peptide-1 Receptor Agonists: A State-of-the-Art Narrative Review | Drugs
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Rating: 4.756 (1925 reviews)

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One of the most comprehensive guides to AI and deep learning available. All concepts covered clearly and concisely with illustrations. Complex concepts are broken down into understandable terms. Even though ML and AI require some complex math, you won’t need it to get idea of what’s going on inside the computer’s “brain”. I use this as a reference when teaching AI concepts and preparing presentations. I highly recommend it

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Reviewed in the United States on April 5, 2025

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Rafael Azevedo Souza Costa

Dallas, US

★★★★★ 5

Great examples and practical explanations

Format: Paperback

Excellent book. Highly recommend

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Reviewed in the United States on May 19, 2025

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Thomas

Natrona Heights, US

★★★★★ 5

Great for intuitive understanding

Format: Paperback

Amazing book. Great examples and diagrams. If you're looking to get an intuitive grasp of deep learning, look no further. If you're an engineer looking to apply it, I would recommend pairing this with one of the more technical canonical texts and a programming focused book.

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Reviewed in the United States on May 4, 2024

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La Monte HP Yarroll

Charlottesville, US

★★★★★ 5

Taught me backpropagation

Format: Paperback

I'm finding this book a great reference to supplement Syracuse IST 691 Deep Learning in Practice. In particular, its explanation of backpropagation is the clearest I have found yet, and that even includes 3 Blue 1 Brown, which always does excellent explanations.

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Reviewed in the United States on April 13, 2025

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3rd Act

Louisville, US

★★★★★ 5

An Easy Read

Format: Paperback

A good narrative description of how the various systems work. Good for getting a conceptual understanding. No math to speak of, and if you want it, you can refer to the references cited, or your other favorite machine learning book.

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Reviewed in the United States on May 7, 2024