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90305660
Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases
物品 #:
90305660
HKD 496
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產品詳情
- 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 FreeKey FeaturesDiscover new and updated content on NLP transformers, PyTorch, and computer vision modelingIncludes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutionsImplement ML models, such as neural networks and linear and logistic regression, from scratchBook DescriptionThe 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. You’ll 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.What you will learnFollow machine learning best practices throughout data preparation and model developmentBuild and improve image classifiers using convolutional neural networks (CNNs) and transfer learningDevelop and fine-tune neural networks using TensorFlow and PyTorchAnalyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIPBuild classifiers using support vector machines (SVMs) and boost performance with PCAAvoid overfitting using regularization, feature selection, and moreWho this book is forThis 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.Table of ContentsGetting Started with Machine Learning and PythonBuilding a Movie Recommendation EnginePredicting Online Ad Click-Through with Tree-Based AlgorithmsPredicting Online Ad Click-Through with Logistic RegressionPredicting Stock Prices with Regression AlgorithmsPredicting Stock Prices with Artificial Neural NetworksMining the 20 Newsgroups Dataset with Text Analysis TechniquesDiscovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic ModelingRecognizing Faces with Support Vector MachineMachine Learning Best PracticesCategorizing Images of Clothing with Convolutional Neural NetworksMaking Predictions with Sequences Using Recurrent Neural NetworksAdvancing Language Understanding and Generation with Transformer ModelsBuilding An Image Search Engine Using Multimodal ModelsMaking Decisions in Complex Environments with Reinforcement Learning
| Publisher | Packt Publishing |
| Publication date | 31 July 2024 |
| Edition | 4th |
| Language | English |
| Print length | 518 pages |
| ISBN-10 | 1835085628 |
| ISBN-13 | 978-1835085622 |
| Item weight | 960 g |
| Dimensions | 19.05 x 3.02 x 23.5 cm |
產品敘述
Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases
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AI & Machine Learning Editorial Review
Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases is an impressive resource that demystifies complex concepts for learners at any level. With a perfect balance between theory and practical application, this 518-page book focuses on real-world use cases, whether it’s stock price forecasting or building recommendation systems. The author, Yuxi (Hayden) Liu, leverages his extensive experience from Google to provide clear, step-by-step guidance on various machine learning algorithms, making it easier for readers to grasp and apply these concepts in real projects. This book is truly a gem for anyone eager to deepen their machine learning expertise.
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優點
- Engaging real-world examples enhance learning
- Covers both fundamentals and advanced topics
- Clear explanations of complex algorithms
- Hands-on projects for practical understanding
- Written by an industry expert with extensive experience
缺點
- Some readers might prefer more theoretical depth.
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特色和優勢
- Comprehensive guide for all levels in machine learning.
- Emphasizes machine learning best practices throughout.
- Includes hands-on examples using PyTorch, TensorFlow, and scikit-learn.
- Covers advanced techniques like NLP transformers and multimodal models.
- Provides insights from an experienced Google ML engineer.
- Free PDF copy included with print or Kindle purchase.
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