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Artificial Intelligence

Darwin Among the Machines

Darwin Among the Machines

(9780718194574) - Dimensions: 198 x 129 x 17 mm - ISBN: 9780718194574 - Number of pages: 304 - Penguin Books Ltd - Weight: 223 g | Nature and machines, Vignettes about Olaf Stapledon

Classic Computer Science Problems in Python

Classic Computer Science Problems in Python

(9781617295980) - David Kopec - Manning Publications - Height: 187 mm - Spine width: 10 mm - Width: 190 mm - ISBN: 9781617295980 - Number of pages: 206 - Languages: English - Publication date: 15 Mar 2019 | Classic Computer

We have been harmonised

We have been harmonised

(9781913083007) - Dimensions: 129 x 198 x 24mm | 274g - Height: 130 mm - Spine width: 21 mm - Width: 198 mm - Old Street Publishing - London, United Kingdom - Old St Publishing - ISBN: 9781913083007 - Number of pages: 320

Neural Networks for Babies

Neural Networks for Babies

(9781492671206) - Height: 202 mm - Spine width: 19 mm - Width: 203 mm - Sourcebooks Jabberwocky - ISBN: 9781492671206 - Number of pages: 24 - Languages: English - Weight: 352 g

The AI Delusion

The AI Delusion

(9780198824305 / 51971867) - Dimensions: 202 x 137 x 19 mm - First edition - Oxford University Press - Height: 137 mm - Spine width: 22 mm - Width: 203 mm - ISBN: 9780198824305 - Number of pages: 249 | Computers do not think

I Still Dream Paperback

I Still Dream Paperback

(9780007541973) - Dimensions: 198 x 129 x 25 mm - Edition: 1st paperback ed - Height: 129 mm - Spine width: 29 mm - Width: 197 mm - The Borough Press - HarperCollins Publishers - ISBN: 9780007541973 - Number of pages: 386

Reinforcement Learning: An Introduction

Reinforcement Learning: An Introduction

(9780262039246) - Dimensions: 178 x 229 x 38mm | 1,179.34g - Second edition - The MIT Press - xxii, 526 - Massachusetts, United States - Format: Hardback | 552 pages - Publication date: 13 Nov 2018 - Height: 236mm

Gaussian Processes for Machine Learning

Gaussian Processes for Machine Learning

Explore Gaussian Processes for Machine Learning and master probabilistic modeling with intuitive explanations, practical examples, and advanced inference techniques that empower data scientists to build robust predictive models.


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