Aprende Machine Learning con Scikit-Learn, Keras y TensorFlow

Thanks to several groundbreaking achievements, deep learning has given a huge boost to the entire field of machine learning. Now even programmers who know almost nothing about this technology can use simple and powerful tools to implement programs capable of learning from data. This best seller 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 variety of techniques ranging from simple linear regression to deep neural networks. There are code samples and exercises throughout the book to help you apply what you've learned, all you need to get started is programming experience: * Use Scikit-Learn to track a sample machine learning project from start to finish * Explore various models, including support vector machines, decision trees, random forests, and assembly methods. * Take advantage of unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection. * Dive into neural network architectures, including convolutional networks, recurrent networks, antagonistic generative networks, autoencoders, diffusion models, and transformers. * Use TensorFlow and Keras to build and train neural networks for computer vision, natural language processing, generative models, and deep reinforcement learning.