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Vara #:
53210962
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems
88% of respondents would recommend this to a friend
Vara #:
53210962
ISK 12556
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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
Comprehensive Coverage
This book offers an in-depth exploration of essential machine learning concepts, tools, and techniques, making it suitable for beginners and experienced practitioners alike looking to build intelligent systems.
Hands-On Approach
With practical examples and exercises, readers gain hands-on experience in applying Scikit-Learn, Keras, and TensorFlow, ensuring they can effectively implement machine learning projects in real-world scenarios.
Up-to-Date Content
Published in November 2022, this book incorporates the latest advancements in machine learning, providing readers with current strategies and practices that reflect today's rapidly evolving technology landscape.
Upplýsingar um vöru
- 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 end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
| 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.
-
Data Scientists
Professionals looking to enhance their practical skills in building machine learning models using popular libraries like TensorFlow and Keras.
-
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.
-
Casual Readers
Individuals seeking light reading or non-technical content may find the technical details and hands-on approach overwhelming.
-
Non-Technical Users
Users without a background in programming or data analysis will struggle to grasp the book's concepts and exercises.
VÖRULÝSING
Ertu með fyrirspurn? Spjallaðu við okkur
Spurningar og svör viðskiptavina
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spurningu:
What programming language is used in this book?
svara: The book uses Python programming language with Scikit-learn, Keras, and TensorFlow frameworks. -
spurningu:
Do I need prior experience in deep learning?
svara: No, the book is suitable for programmers with little to no prior experience in deep learning. -
spurningu:
Does the book cover unsupervised learning techniques?
svara: Yes, the book covers unsupervised learning techniques such as clustering, dimensionality reduction, and anomaly detection.
AI & Machine Learning Editorial Review
The "HandsâOn Machine Learning with ScikitâLearn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems" book has received a mixture of positive and negative feedback. The majority of the reviews praise the book for its comprehensive and practical approach to machine learning and AI. Customers appreciate the practical examples and the high density of information, with one reviewer even calling it a masterpiece. The book is described as a substantial and invaluable resource for anyone working on machine learning projects. Clear explanations and working code examples are also highlighted as key positives. However, there are also complaints about the packaging and protection of the book during delivery, as well as the overwhelming amount of content, which some found challenging to retain. A potential need for supplementary resources or further reading is also mentioned.
Customer Reviews & Ratings
868 einkunnir viðskiptavina
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5 stjarna
83%
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4 stjarna
10%
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3 stjarna
3%
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2 stjarna
1%
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1 stjarna
3%
Umsögn um þessa vöru
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Kostir
- Comprehensive and practical approach to machine learning and AI
- High density of information
- Substantial and invaluable resource for machine learning projects
- Clear explanations and working code examples
Gallar
- Overwhelming amount of content
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ISK 12556
Pantaðu núna og fáðu það í kringum Fimmtudagur, Júlí 23
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Eiginleikar og kostir
- 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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