|
Showing 1 - 1 of
1 matches in All Departments
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 best-selling 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 Aurelien Geron 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 machine learning 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, and transformers Use TensorFlow
and Keras to build and train neural nets for computer vision,
natural language processing, generative models, and deep
reinforcement learning Train neural nets using multiple GPUs and
deploy them at scale using Google's Vertex AI
|
You may like...
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
Catan
(16)
R1,150
R889
Discovery Miles 8 890
|