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Python Deep learning: Develop your first Neural Network in Python Using TensorFlow, Keras, and PyTorch (Step-by-Step Tutorial for Beginners)
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PYG 188632
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What Stands Out
Detalles de producto
- Learn how to develop your first neural network in Python
- Step-by-step tutorial for beginners
- Uses popular deep learning frameworks like TensorFlow, Keras, and PyTorch
- Gain practical experience in implementing deep learning models
- Develop a solid understanding of deep learning concepts
- Written in a beginner-friendly way with clear explanations and examples
| Publisher | Independently published |
| Publication date | April 3, 2019 |
| Language | English |
| Print length | 170 pages |
| ISBN-10 | 1092562222 |
| ISBN-13 | 978-1092562225 |
| Item Weight | 8.3 ounces (235.31 grams) |
| Dimensions | 6 x 0.39 x 9 inches (15.2 x 1 x 22.9 cm) |
Who Should Buy?
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Beginners in Coding
Ideal for those new to programming, offering step-by-step guidance on building neural networks.
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Data Science Students
Perfect for students who need practical insights into deep learning with popular Python frameworks.
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Aspiring AI Developers
Great for individuals looking to enter the AI field, providing foundational knowledge for neural network development.
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Advanced Practitioners
Not suitable for experts, as it covers basic concepts and lacks in-depth technical detail.
DESCRIPCIÓN DEL PRODUCTO
Python Deep learning: Develop your first Neural Network in Python Using TensorFlow, Keras, and PyTorch (Step-by-Step Tutorial for Beginners)
About This Item
Introducing "Python Deep learning: Develop your first Neural Network in Python Using TensorFlow, Keras, and PyTorch" – the perfect step-by-step tutorial for beginners! Are you interested in deep learning and neural networks? Look no further! This comprehensive guide is designed to help beginners like you develop your first neural network using popular libraries such as TensorFlow, Keras, and PyTorch. With Python Deep learning, you'll learn how to harness the power of these frameworks to build your own neural networks. Whether you're a student, a professional, or simply someone who wants to dive into the exciting field of deep learning, this book has everything you need to get started. Why choose this book? We've carefully crafted this tutorial to be accessible and easy to understand, even for those with no prior experience. Step-by-step instructions and clear explanations will guide you through the development process, ensuring that you grasp each concept fully. Here are just a few of the key topics covered in Python Deep learning: - Understanding deep learning and neural networks - Exploring TensorFlow, Keras, and PyTorch - Implementing deep learning techniques with Python - Building and training neural network models - Comparing TensorFlow, Keras, and PyTorch for deep learning - Applying deep learning to computer vision, natural language processing, image recognition, and more! By the end of this book, you'll have the confidence and knowledge to start developing your own deep learning projects.
Whether you're interested in computer vision, natural language processing, or any other application of deep learning, Python Deep learning will equip you with the skills you need. Don't miss out on the opportunity to learn from industry experts and take your first steps into the world of deep learning. Order your copy of "Python Deep learning: Develop your first Neural Network in Python Using TensorFlow, Keras, and PyTorch" today and unlock your potential in the exciting field of deep learning.
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Neural Networks Editorial Review
The Python Deep Learning book covers the basics of neural networks using TensorFlow, Keras, and PyTorch. The book lacks an overview of neural networks; Chapter 2 is only two pages. The author focuses on providing the code examples rather than explaining the concepts behind them - leaving readers to look up the instruction and attempt to figure out what is going on. The book has errors in the code; for example, page 43 adds two extra lines of code that cause errors, and the same block of code also imports Numpy, even though it isn't used. The book may be useful as long as readers' expectations are clear, and they don't expect much in-depth educative value.
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ventajas
- Beginner-friendly introduction
- Doesn't require a good understanding of calculus or Algebra.
Contras
- Lack of an overview of neural networks
Product Price History
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PYG 188632
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características y beneficios
- Learn the fundamentals of Deep learning to build your own Neural Network in Python
- Recommended for beginners who want to transition to a professional level
- Suitable for students and academicians focusing on python programming, neural networks, machine learning, and deep learning
- Covers TensorFlow, Keras, PyTorch, and Theano libraries
- Provides Python codes and corresponding explanations for each task
- An affordable alternative to big and boring textbooks
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