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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems
KRW 167212
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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.
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What Stands Out
제품 세부 정보
- Book designed for readers with Python programming experience and familiarity with main scientific libraries
- Suitable for those interested in machine learning applications such as customer segmentation, product recommendation, fraud detection, and revenue forecasting
- Intended to provide concepts, tools, and intuition needed to implement programs capable of learning from data
- Covers a wide range of techniques, from simple to deep learning algorithms, utilizing production-ready Python frameworks like Scikit-Learn, TensorFlow, and Keras
- Scikit-Learn is user-friendly and implements many machine learning algorithms efficiently, making it a great entry point to learning machine learning
- TensorFlow and Keras offer more complex and high-level deep learning capabilities, with TensorFlow supporting large-scale machine learning applications
| Publisher | O'Reilly Media |
| Publication date | 31 Oct. 2022 |
| Edition | 3rd |
| Language | English |
| Print length | 861 pages |
| ISBN-10 | 1098125975 |
| ISBN-13 | 978-1098125974 |
| Item weight | 1.36 kg |
| Dimensions | 18.42 x 5.08 x 24.13 cm |
Who Should Buy?
-
Beginner Learners
Newcomers to machine learning who want a comprehensive introduction with practical examples and clear explanations will benefit greatly.
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Data Scientists
Professionals looking to enhance their practical skills in building machine learning models using popular libraries like TensorFlow and Keras.
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Computer Science Students
University students studying machine learning concepts who need a resourceful guide with hands-on coding exercises and projects.
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Advanced Practitioners
Experienced data scientists may find the content too basic and lacking in depth for their advanced needs.
제품 설명
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems
고객 질문 및 답변
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의문:
What programming language is used in this book?
답변: The book uses Python programming language with Scikit-learn, Keras, and TensorFlow frameworks. -
의문:
Do I need prior experience in deep learning?
답변: No, the book is suitable for programmers with little to no prior experience in deep learning. -
의문:
Does the book cover unsupervised learning techniques?
답변: Yes, the book covers unsupervised learning techniques such as clustering, dimensionality reduction, and anomaly detection.
AI & Machine Learning Editorial Review
Hands-On Machine Learning With Scikit-Learn, Keras, And TensorFlow 3e: Concepts, Tools, And Techniques To Build Intelligent Systems is an essential guide for anyone looking to dive into the world of machine learning. This comprehensive text, published by O'Reilly Media, spans an impressive 861 pages filled with valuable insights and practical examples that enhance understanding of foundational concepts, algorithms, and model implementations. Readers have noted that Geron's clear writing and systematic approach make it suitable for both beginners and experienced practitioners, leading to a deep grasp of machine learning techniques in Python. While some have mentioned that the book may become dated with the rapid advancement in tools like Keras and TensorFlow, the fundamental knowledge it provides is deemed invaluable for future applications in the field.
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장점
- In-depth explanations of foundational concepts
- Covers a wide range of machine learning topics
- Clear writing style for better understanding
- Includes practical applications and code samples
- Highly recommended for both beginners and experts
단점
- Some content may become dated with tool updates
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KRW 167212
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특징 및 장점
- Book explores machine learning techniques using Scikit-learn, Keras and TensorFlow
- Suitable for programmers with no prior experience in deep learning
- Covers a range of models from simple linear regression to deep neural networks
- Includes code examples and exercises throughout the book
- Dives into neural net architectures for computer vision, NLP, generative models, deep reinforcement learning
- Explores unsupervised learning techniques like clustering, dimensionality reduction and anomaly detection
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