Building Neural Networks from Scratch with Python
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Building Neural Networks from Scratch with Python
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Narrated by:
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Bryan Hughey
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By:
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L.D. Knowings
About this listen
Unlock the World of Neural Networks in Python!
Ready to dive into AI and machine learning? This guide makes it easy, even if you're a Python pro or a total beginner!
Discover how to:
- Understand the fundamentals of neural networks and their benefits
- Code without drowning in complex math equations
- Become a debugging master for efficient coding and data testing
- Stay updated on the latest tech trends and advancements
- Demystify layers, gradients, and tackle underfitting/overfitting
Transform your coding skills and knowledge with beginner-friendly projects!
Imagine a world where machine learning is accessible to all, including you. This guide will change how you perceive neural networks and propel you confidently into the realm of coding!
Don't miss this opportunity! Master neural networks and make a difference in machine learning. Click "Add to Cart" now!
PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
©2023 Keyven Lewis (P)2024 Keyven LewisListeners also enjoyed...
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What listeners say about Building Neural Networks from Scratch with Python
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Overall5 out of 5 stars
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Performance5 out of 5 stars
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Story5 out of 5 stars
- Brendan Brady
- 23-02-24
Perfect
I've read a lot of books, both fiction and non-fiction. In fact, I typically read a book a week. But never have I read a text which is so clear, and concise. Such a perfect marriage of simplicity and information about the subject.
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Overall5 out of 5 stars
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Story5 out of 5 stars
- Millie Little
- 20-02-24
Coverage of Core Concepts
The book should cover core concepts of neural networks, including feed-forward and back-propagation, different activation functions, regularization techniques, and optimization algorithms.
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- Alexandra Lloyd
- 20-02-24
Up-to-Date Information
Given the rapidly evolving field of deep learning, a good book should cover contemporary techniques and best practices. It should also address popular libraries and frameworks commonly used in Python, such as TensorFlow or PyTorch.
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Story5 out of 5 stars
- Cameron Saunders
- 20-02-24
Practical Examples
Readers often appreciate books that not only explain theory but also provide practical examples and code snippets. These examples should be well-commented and easy to follow.
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Overall5 out of 5 stars
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Story5 out of 5 stars
- Lorraine Vega
- 20-02-24
Clarity and Explanation
A good book should explain complex concepts in a clear and understandable manner. It should not assume too much prior knowledge and should build up from fundamentals to more advanced topics.
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Overall5 out of 5 stars
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Performance5 out of 5 stars
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Story5 out of 5 stars
- Damon Chandler
- 23-02-24
Clear Explanations of Algorithms
The book excels in providing clear explanations of neural network algorithms and techniques. Complex algorithms are broken down into manageable steps, with detailed explanations of each component. This clarity helps demystify neural networks and enables readers to understand not just how algorithms work, but why they work, empowering them to adapt and apply these techniques to new problems.
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Overall5 out of 5 stars
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Story5 out of 5 stars
- Karen Reid
- 23-02-24
In-depth Exploration of Frameworks
The book provides an in-depth exploration of popular neural network frameworks, such as TensorFlow and PyTorch. Readers learn how to implement neural network models using these frameworks, gaining hands-on experience with industry-standard tools and techniques. By familiarizing themselves with these frameworks, readers are better positioned to leverage their capabilities and stay current with advancements in neural network development.
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Overall5 out of 5 stars
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Story5 out of 5 stars
- Anonymous User
- 21-02-24
Integration with Other Technologies
Neural networks are often used in conjunction with other technologies, such as big data platforms, cloud computing, or edge devices. A book that explores how neural networks integrate with these technologies can give readers a more holistic understanding of their practical applications.
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Overall5 out of 5 stars
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- Alicia Gonzales
- 20-02-24
Hands-On Exercises
Exercises and projects help reinforce learning. A good book might include exercises at the end of each chapter or provide larger projects that readers can work on to deepen their understanding.
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Overall5 out of 5 stars
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- Betty Alvarado
- 21-02-24
Historical Context and Milestones
Providing historical context by discussing the evolution of neural network architectures, key milestones, and influential research papers can help readers appreciate the progress and significance of the field.
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