


David. Dichter und KönigDAVID Dichter und Knig In Zusammenarbeit mit dem LIT Verlag ""Was Sie hier in Hnden haben, ist eigentlich ein Vielfaches. Zunchst ist es die Geschichte von Menschen, von einzelnen Menschen und den oft ergreifenden Schicksalen ihres Lebens, die wir ich meine damit den Leser und schliee mich selber mit ein beim Lesen der folgenden Seiten erleben und miterleben drfen. Dieses Erleben und Miterleben dessen, was den Betroffenen widerfahren ist, halte ich
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4.2 ★★★★★
Based on 1047 reviews
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Product Reviews
★★★★★ 5
Before GPT can chat, it has to learn.
Format: Paperback
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
★★★★★ 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
★★★★★ 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
★★★★★ 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
★★★★★ 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
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