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Building Neural Networks from Scratch with Python

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Building Neural Networks from Scratch with Python

By: L.D. Knowings
Narrated by: Bryan Hughey
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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 Lewis Computer Science Machine Theory & Artificial Intelligence Programming Programming & Software Development Software Development Machine Learning Data Science Artificial Intelligence Software

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The book should cover core concepts of neural networks, including feed-forward and back-propagation, different activation functions, regularization techniques, and optimization algorithms.

Coverage of Core Concepts

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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.

Perfect

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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.

Up-to-Date Information

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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.

Practical Examples

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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.

Clarity and Explanation

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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.

Clear Explanations of Algorithms

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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.

In-depth Exploration of Frameworks

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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.

Integration with Other Technologies

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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.

Hands-On Exercises

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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.

Historical Context and Milestones

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