Aurelien Geron – författare

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 endExplore several models, including support vector machines, decision trees, random forests, and ensemble methodsExploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detectionDive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, and transformersUse TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learningTrain neural nets using multiple GPUs and deploy them at scale using Google's Vertex AI

The potential of machine learning today is extraordinary, yet many aspiring developers and tech professionals find themselves daunted by its complexity. Perhaps you're ready to jump in, but you're unsure where or how to begin. Whether you're looking to enhance your skill set and apply machine learning to real-world projects or are simply curious about how AI systems function, this book is your jumping-off place.In a way that's approachable yet deeply informative, author Aurelien Geron delivers the ultimate introductory guide to machine learning and deep learning. With a focus on clear explanations and real-world Python examples, the book takes you through cutting-edge tools like scikit-learn and PyTorch-from basic regression techniques to advanced neural networks like transformers and generative adversarial networks. Whether you're a student, professional, or hobbyist, you'll gain the skills to begin building intelligent systems.Understand ML basics, including concepts like overfitting and hyperparameter tuningLearn to build end-to-end ML projects using scikit-learn, from data exploration to model evaluationExplore advanced architectures like convolutional and recurrent neural networks with PyTorchDiscover techniques for unsupervised learning, such as clustering and anomaly detectionIncrease your expertise in state-of-the-art AI systems by fine-tuning pretrained modelsBuild tangible skills with complete hands-on coding exercises and real-world applications

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 endExplore several models, including support vector machines, decision trees, random forests, and ensemble methodsExploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detectionDive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformersUse TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning

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 endExplore several models, including support vector machines, decision trees, random forests, and ensemble methodsExploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detectionDive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformersUse TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning

The potential of machine learning today is extraordinary, yet many aspiring developers and tech professionals find themselves daunted by its complexity. Whether you're looking to enhance your skill set and apply machine learning to real-world projects or are simply curious about how AI systems function, this book is your jumping-off place.With an approachable yet deeply informative style, author Aurlien Gron delivers the ultimate introductory guide to machine learning and deep learning. Drawing on the Hugging Face ecosystem, with a focus on clear explanations and real-world examples, the book takes you through cutting-edge tools like Scikit-Learn and PyTorchfrom basic regression techniques to advanced neural networks. Whether you're a student, professional, or hobbyist, you'll gain the skills to build intelligent systems.Understand ML basics, including concepts like overfitting and hyperparameter tuningComplete an end-to-end ML project using scikit-Learn, covering everything from data exploration to model evaluationLearn techniques for unsupervised learning, such as clustering and anomaly detectionBuild advanced architectures like transformers and diffusion models with PyTorchHarness the power of pretrained modelsincluding LLMsand learn to fine-tune themTrain autonomous agents using reinforcement learning