Descriere YEO:
Pe YEO găsești Hands-On Machine Learning with Scikit-Learn, de la Aurlien Gron, în categoria Computers.
Indiferent de nevoile tale, Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems - Géron din categoria Computers îți poate aduce un echilibru perfect între calitate și preț, cu avantaje practice și moderne.
Preț: 474.25 Lei
Caracteristicile produsului Hands-On Machine Learning with Scikit-Learn,
- Brand: Aurlien Gron
- Categoria: Computers
- Magazin: libris.ro
- Ultima actualizare: 25-02-2025 01:30:22
Comandă Hands-On Machine Learning with Scikit-Learn, Online, Simplu și Rapid
Prin intermediul platformei YEO, poți comanda Hands-On Machine Learning with Scikit-Learn, de la libris.ro rapid și în siguranță. Bucură-te de o experiență de cumpărături online optimizată și descoperă cele mai bune oferte actualizate constant.
Descriere magazin:
Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (
Scikit-
Learn,
Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aurélien Géron explores a range of techniques, starting
with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you\'ve learned. Programming experience is all you need to get started. 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