3rd Edition
by Aurélien Géron (Author)
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
