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Practical MATLAB Deep Learning: A Project-Based Approach 1st ed. Edition
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What Stands Out
Product Details
| Publisher | Apress |
| Publication date | February 8, 2020 |
| Edition | 1st ed. |
| Language | English |
| Print length | 267 pages |
| ISBN-10 | 1484251237 |
| ISBN-13 | 978-1484251232 |
| Item Weight | 1.05 pounds (480 grams) |
| Dimensions | 7.01 x 0.63 x 10 inches (17.8 x 1.6 x 25.4 cm) |
Who Should Buy?
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Students in AI
Ideal for university students pursuing degrees related to artificial intelligence and machine learning using MATLAB.
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Professional Developers
Perfect for software developers looking to enhance their skills in deep learning applications with practical MATLAB projects.
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Research Scientists
Useful for researchers who want to implement deep learning in their studies using MATLAB-based tools and frameworks.
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Beginners in Programming
Not suitable for those with no programming experience, as deep learning concepts require a foundational understanding of coding.
Product Description
Practical MATLAB Deep Learning: A Project-Based Approach 1st ed. Edition
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Intelligence & Semantics Editorial Review
**Editorial Review of Practical MATLAB Deep Learning: A Project-Based Approach** "Practical MATLAB Deep Learning: A Project-Based Approach" has elicited a mixed response from customers who have attempted to utilize it as a resource for mastering MATLAB. While some readers appreciate the practical examples provided in the book, especially those who use MATLAB in their daily work, others have expressed frustration with aspects of the content. Notably, several users have encountered issues with the accuracy of provided code examples, leading to confusion and obstacles while trying to implement the lessons effectively. Critics have pointed out significant errors, such as the inclusion of non-existent MATLAB commands like "PlotSet," which necessitates manual corrections to the code in order for it to run smoothly. This not only detracts from the learning experience but also raises questions about the reliability of the instructional material. Overall, while the book may have strengths in its approach to practical applications, these issues with coding errors hinder its overall utility for learners seeking a straightforward guide to deep learning in MATLAB. **
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Pros
- Practical examples that are relevant for users with daily MATLAB experience.
- Focus on a project-based learning approach.
Cons
- Significant inaccuracies in code examples, including non-existent commands.
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