Machine Learning Libro

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
62,00 € EN AMAZON

Machine Learning Libro

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Última actualización: 24 de agosto de 2025

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