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Deep Learning for Beginners: A beginner's guide to getting up and running with deep learning from scratch using Python
86% of respondents would recommend this to a friend
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Product Details
| Publisher | Packt Publishing (September 18, 2020) |
| Language | English |
| Paperback | 432 pages |
| ISBN-10 | 1838640851 |
| ISBN-13 | 978-1838640859 |
| Item Weight | 1.65 pounds (750 grams) |
| Dimensions | 9.25 x 7.52 x 0.9 inches (23.5 x 19.1 x 2.3 cm) |
Who Should Buy?
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Aspirational Data Scientists
Individuals aiming to start a career in data science will find foundational knowledge and practical skills in this guide.
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Python Beginners
Those new to programming will appreciate the practical examples outlined in Python, making concepts easier to grasp.
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Self-learners
Individuals who prefer self-study can navigate through the structured content at their own pace to build deep learning skills.
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Advanced Practitioners
Experienced deep learning professionals may find the content too basic and lacking in advanced topics and methodologies.
Product Description
Deep Learning for Beginners: A beginner's guide to getting up and running with deep learning from scratch using Python
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Intelligence & Semantics Editorial Review
In the realm of deep learning literature, "Deep Learning for Beginners" by Dr. Pablo Rivas is earning commendations as a comprehensive introduction tailored for aspiring practitioners in the field. Readers laud the book's clear, structured approach that seamlessly transitions from foundational machine learning concepts to advanced deep learning techniques. Dr. Rivas successfully demystifies complex topics such as TensorFlow setup, data preparation, and model training through step-by-step instructions and practical code examples. The use of Google Colab for hands-on experiments is particularly appreciated, as it democratizes access to machine learning tools, allowing readers to experiment without the need for extensive local setup. The book covers a wide range of topics indispensable for anyone looking to venture into deep learning, including supervised and unsupervised learning models, training techniques, various neural network architectures like convolutional and recurrent networks, and even sophisticated concepts such as variational autoencoders and generative adversarial networks. This breadth ensures that readers not only acquire necessary theoretical knowledge but also practical insights into implementation. Additionally, the author’s commitment to social responsibility—donating proceeds to Latinx in AI—adds a commendable layer of purpose to the work, resonating with readers who value community engagement in tech. Overall, "Deep Learning for Beginners" is warmly received as a vital resource for new learners. It is recommended for anyone looking to grasp the essentials of deep learning in an organized and engaging manner. **
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Pros
- Clear and structured approach to complex topics
- Step-by-step guided instructions with practical examples
- Covers a wide range of fundamental and advanced concepts in deep learning
- Hands-on examples using Google Colab for practical experience
- Commitment to social responsibility through donations to Latinx in AI
Cons
- Limited mention of advanced techniques for experienced users
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