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Machine Learning Book

I 10 migliori prodotti di giugno 2025
Ultimo aggiornamento: 19 giugno 2025
Andriy Burkov
Andriy Burkov
The Hundred-Page Machine Learning Book          
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#1 VINCITORE
O'Reilly Media, Inc.
O'Reilly Media, Inc.
Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems
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  • Use scikit-learn to track an example ML project end to end
  • Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
  • Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
  • Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
  • Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
  • 69,93 € SU AMAZON
MIT Press
MIT Press
Deep Learning                                    
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#3

  • Language Published: English
  • Binding: hardcover
  • It ensures you get the best usage for a longer period
  • 72,00 € SU AMAZON
Andriy Burkov
Andriy Burkov
The Hundred-Page Machine Learning Book in italiano
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#4
Independently published
Independently published
Machine Learning Foundations and Applications: A Practical Guide to Supervised, Unsupervised, and Reinforcement Learning
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#5
Packt Publishing
Packt Publishing
Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
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#6
Cambridge University Press
Cambridge University Press
Mathematics for Machine Learning                
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#7
Independently published
Independently published
Machine Learning for Absolute Beginners: A Plain English Introduction (Third Edition): 1
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#8
Independently published
Independently published
Machine Learning with Python: A Practical Beginners’ Guide
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#9
O'Reilly UK Ltd.
O'Reilly UK Ltd.
Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
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#10
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