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Yuxi (Hayden)
Python Machine Learning By Example
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4983997Python Machine Learning By Examplehttps://www.gandhi.com.mx/python-machine-learning-by-example-9781835082225/phttps://gandhi.vtexassets.com/arquivos/ids/4534867/image.jpg?v=638890707651900000679755MXNPackt PublishingInStock/Ebooks/4720771Python Machine Learning By Example679755https://www.gandhi.com.mx/python-machine-learning-by-example-9781835082225/phttps://gandhi.vtexassets.com/arquivos/ids/4534867/image.jpg?v=638890707651900000InStockMXN99999DIEbook20249781835082225_W3siaWQiOiI3NmYzNGE5NS1mZGYwLTQ1OGMtYmI1OS01YmUwZTBiMWNkZjYiLCJsaXN0UHJpY2UiOjc1NSwiZGlzY291bnQiOjc2LCJzZWxsaW5nUHJpY2UiOjY3OSwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjQtMDctMzFUMDA6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9781835082225_<p><b>Learn machine learning (ML) with this hands-on guide from best-selling author and ex-Google ML engineer Yuxi (Hayden) Liu. He teaches the basics of ML algorithms to NLP transformers and multimodal models with best practice tips and real-world examples</b></p><h2>Key Features</h2><ul><li>New and updated content on NLP transformers, PyTorch, and computer vision modeling</li><li>Best practices have expanded beyond one chapter with tips to improve your ML solutions showcased throughout the book</li><li>Implement ML algorithms, such as neural networks and decision trees from scratch</li></ul><h2>Book Description</h2>The fourth edition of Python Machine Learning by Example is a comprehensive guide for beginners and experienced ML practitioners who want to learn more advanced techniques like multimodal modeling. Written by best-selling author and ex-Google ML engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for ML engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on NLP transformers with BERT and GPT-4 and multimodal computer vision models with PyTorch and Hugging Face. Youll learn advanced modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your ML expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.<h2>What you will learn</h2><ul><li>Follow machine learning best practices for data preparation, model training, and evaluation</li><li>Build and improve image classifiers using CNNs, transfer learning, and data augmentation</li><li>Build and fine-tune neural networks using TensorFlow and PyTorch for stock price prediction and image search</li><li>Analyze sequence data and make predictions using RNNs and transformers</li><li>Build classifiers using SVMs and boost performance with principal component analysis</li><li>Learn to avoid overfitting using cross-validation, regularization, feature selection, and dimensionality reduction</li></ul><h2>Who this book is for</h2><p>This expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python knowledge. The real-world lessons and code prepare anyone undertaking their first serious ML project.</p>...(*_*)9781835082225_<p><b>Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandas</b></p><h2>Key Features</h2><ul><li>Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling</li><li>Includes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutions</li><li>Implement ML models, such as neural networks and linear and logistic regression, from scratch</li><li>Purchase of the print or Kindle book includes a free PDF copy</li></ul><h2>Book Description</h2>The fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. Youll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.<h2>What you will learn</h2><ul><li>Follow machine learning best practices throughout data preparation and model development</li><li>Build and improve image classifiers using convolutional neural networks (CNNs) and transfer learning</li><li>Develop and fine-tune neural networks using TensorFlow and PyTorch</li><li>Analyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIP</li><li>Build classifiers using support vector machines (SVMs) and boost performance with PCA</li><li>Avoid overfitting using regularization, feature selection, and more</li></ul><h2>Who this book is for</h2><p>This expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.</p>...(*_*)9781835082225_<p><b>Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandas. Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free</b></p><h2>Key Features</h2><ul><li>Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling</li><li>Includes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutions</li><li>Implement ML models, such as neural networks and linear and logistic regression, from scratch</li></ul><h2>Book Description</h2>The fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. Youll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.<h2>What you will learn</h2><ul><li>Follow machine learning best practices throughout data preparation and model development</li><li>Build and improve image classifiers using convolutional neural networks (CNNs) and transfer learning</li><li>Develop and fine-tune neural networks using TensorFlow and PyTorch</li><li>Analyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIP</li><li>Build classifiers using support vector machines (SVMs) and boost performance with PCA</li><li>Avoid overfitting using regularization, feature selection, and more</li></ul><h2>Who this book is for</h2><p>This expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.</p>...9781835082225_Packt Publishinglibro_electonico_9781835082225_9781835082225Yuxi (Hayden)InglésMéxico2024-07-31T00:00:00+00:00https://getbook.kobo.com/koboid-prod-public/packt-epub-1a2432c4-2da9-485d-bfe9-3c0a002bcc61.epub2024-07-31T00:00:00+00:00Packt Publishing