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64176255
Practical Deep Learning: A Python-Based Introduction, (Paperback)
82% of respondents would recommend this to a friend
Item #:
64176255
ILS 241
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Practical Deep Learning teaches total beginners how to build datasets and train neural networks for DL projects. This is the book you've been waiting for if you've been curious about machine learning.
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What Stands Out
Hands-On Approach
Offers a practical, hands-on learning experience, enabling readers to apply deep learning concepts using Python, making it ideal for beginners seeking applicable skills.
Comprehensive Coverage
Covers a wide array of topics in deep learning, from basics to advanced techniques, ensuring that readers gain a thorough understanding of the field.
Accessible Format
Written in an easy-to-understand language, this book is designed for learners at all levels, making complex concepts more accessible to a broader audience.
Product Details
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | February, 2021 |
| Pages | 464 |
| Reading level | General |
| Subgenre | Data Science |
| Series title | No Series |
| Edition | 1 |
| Publisher | No Starch Press |
| Original languages | English |
| Language | English |
| Edu focus | Technology |
| Educational level | High School |
| Is collectible | N |
| Illustrator | Kevin Howdeshell |
| Binding type | Perfect Binding |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 7.00 x 1.30 x 9.20 in (17.8 x 3.3 x 23.4 cm) |
| Assembled product weight | 1.94 lb (880 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
-
Beginners in AI
Perfect for those new to artificial intelligence and deep learning concepts, providing clear explanations and practical examples.
-
Python Programmers
Ideal for existing Python developers wanting to enhance their skills in deep learning applications using familiar programming language.
-
Data Scientists
Beneficial for data scientists looking to incorporate deep learning techniques into their data analysis and machine learning workflows.
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Advanced Users
Not ideal for experienced deep learning practitioners seeking advanced theories or cutting-edge techniques beyond basic frameworks.
Product Description
Practical Deep Learning: A Python-Based Introduction, (Paperback)
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Customer Questions & Answers
-
Question:
What prerequisites are needed for this book?
Answer: Basic familiarity with computer programming and high school math. -
Question:
What will I learn from this book?
Answer: How to build training datasets, work with machine learning models, understand neural networks, and develop deep learning models from scratch. -
Question:
Does the book focus on theory or practical application?
Answer: The book focuses on practical applications by teaching how to build and train models for deep learning projects.
Ronald T Kneusel All Books Editorial Review
Practical Deep Learning: A Python-Based Introduction is a comprehensive non-fiction book that spans 464 pages, offering readers a valuable insight into the field of deep learning, tailored for individuals at a general reading level. The paperback format ensures ease of use for those studying computing and the broader genre of data science. Published by No Starch Press in February 2021, this book is conducive for high school educational levels, making it accessible for students and educators alike. With its perfect binding and authored with contributions from illustrator Kevin Howdeshell, it stands as a solid reference for technology enthusiasts and learners aiming to grasp deep learning fundamentals.
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Pros
- Comprehensive guide for Python-based deep learning
- Suitable for high school education
- Well-structured for general readers
- Perfect binding ensures durability
- Includes insights into data science applications
Cons
- No series title may limit broader context
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ILS 241
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Features & Benefits
- Teaches beginners to build datasets for neural networks
- Focuses on deep learning subfield of machine learning
- Explains core concepts and foundations
- Inspires exploration of deep learning
- Covers Python, scikit-learn, Keras libraries
- Includes building models from scratch and a final case study
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