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AI Foundations of Neural Networks: Easy To Read Guide Introducing the Foundations Of Neural Networks and AI
Dive into the fascinating world of artificial intelligence with "AI Foundations of Neural Networks." This comprehensive guide demystifies the complex concepts of neural networks, offering a clear and accessible path to understanding the core principles that fuel modern AI systems. From the basic building blocks of neural networks to advanced architectures, this book is designed to provide a thorough grounding in deep learning for readers at all levels of expertise.
Chapters Overview:
The Neuron - The Fundamental Unit: Explore the basic structure that mimics the human brain's neurons, setting the stage for understanding how neural networks operate.Activation Functions - Bringing Neurons to Life: Learn about the functions that help neural networks make decisions, allowing them to process information in complex ways.The Anatomy of Layers: Delve into how layers of neurons work together to process data, forming the backbone of neural network architecture.Backpropagation - Learning from Errors: Understand the mechanism by which neural networks learn from their mistakes, optimizing their performance over time.Loss Functions - Measuring Performance: Discover how neural networks evaluate their accuracy and make adjustments to improve their predictions.Optimization Algorithms - The Road to Convergence: Get to grips with the strategies that guide neural networks towards making more accurate predictions.Overfitting and Generalization: Learn about the challenges of making models that perform well not just on the data they were trained on but on new, unseen data as well.Advanced Architectures: Explore the frontier of neural network design, including the latest models that drive progress in AI research.Why This Book?
"AI Foundations of Neural Networks" stands out as a beacon of knowledge, transforming what might appear as a complex field into a series of comprehensible concepts. With a focus on clarity, practical insights, and intuitive understanding, this book bridges the gap between theoretical knowledge and real-world application. Whether you're a student, professional, or enthusiast eager to navigate the realm of AI, this guide illuminates the path forward.
Embark on a journey through the corridors of deep learning with "AI Foundations of Neural Networks." Unlock the secrets behind the artificial intelligence technologies that are transforming our world. Your exploration of neural networks starts here.
Perfect for: Students, AI professionals, tech enthusiasts, and anyone curious about the inner workings of neural networks and deep learning.
Discover the principles of AI that are shaping the future. Your journey into neural networks begins now.
Chapters Overview:
The Neuron - The Fundamental Unit: Explore the basic structure that mimics the human brain's neurons, setting the stage for understanding how neural networks operate.Activation Functions - Bringing Neurons to Life: Learn about the functions that help neural networks make decisions, allowing them to process information in complex ways.The Anatomy of Layers: Delve into how layers of neurons work together to process data, forming the backbone of neural network architecture.Backpropagation - Learning from Errors: Understand the mechanism by which neural networks learn from their mistakes, optimizing their performance over time.Loss Functions - Measuring Performance: Discover how neural networks evaluate their accuracy and make adjustments to improve their predictions.Optimization Algorithms - The Road to Convergence: Get to grips with the strategies that guide neural networks towards making more accurate predictions.Overfitting and Generalization: Learn about the challenges of making models that perform well not just on the data they were trained on but on new, unseen data as well.Advanced Architectures: Explore the frontier of neural network design, including the latest models that drive progress in AI research.Why This Book?
"AI Foundations of Neural Networks" stands out as a beacon of knowledge, transforming what might appear as a complex field into a series of comprehensible concepts. With a focus on clarity, practical insights, and intuitive understanding, this book bridges the gap between theoretical knowledge and real-world application. Whether you're a student, professional, or enthusiast eager to navigate the realm of AI, this guide illuminates the path forward.
Embark on a journey through the corridors of deep learning with "AI Foundations of Neural Networks." Unlock the secrets behind the artificial intelligence technologies that are transforming our world. Your exploration of neural networks starts here.
Perfect for: Students, AI professionals, tech enthusiasts, and anyone curious about the inner workings of neural networks and deep learning.
Discover the principles of AI that are shaping the future. Your journey into neural networks begins now.
92 pages, Kindle Edition
Published February 4, 2024
2 people want to read
About the author
Jon Adams
116 books6 followersI grew up in Santa Clara Valley, a place that no longer exists under that name. I attended school in Cupertino when there were still some prunes, cherries, and apricots, but no apples then. In my junior year of high school, I dropped out. For my parents took us (me, my two brothers and my sister) on a world cruise. We left San Francisco on the 58-foot schooner Fairweather. We sailed west across the Pacific and Indian Oceans, then up the Red Sea to the Mediterranean. From there we sailed across the Atlantic and Caribbean, passed through the Panama Canal, and then, after four years, returned to San Francisco. This cruise is the basis for the novel, "The Cruise of the Jest."
The basic story in "The Cruise of the Jest" had been on my mind for a long time, but the novel itself is not entirely based on my own experience. My mother kept a journal during the cruise on Fairweather, a journal that I later inherited. Actually, I started writing The Cruise of the Jest after I began transcribing and editing my mother's journal, because I realized that the journal didn't tell a story--journals rarely do. And I knew that if I wanted to describe what it was like to sail around the world, I needed a story. I think this need for a story is an example of fiction being more believable, and certainly more compelling, than simply telling the facts of what happened. The facts of what happened have their own place in my memory, but it takes a story to convey to others a sense of your own experience.
The basic story in "The Cruise of the Jest" had been on my mind for a long time, but the novel itself is not entirely based on my own experience. My mother kept a journal during the cruise on Fairweather, a journal that I later inherited. Actually, I started writing The Cruise of the Jest after I began transcribing and editing my mother's journal, because I realized that the journal didn't tell a story--journals rarely do. And I knew that if I wanted to describe what it was like to sail around the world, I needed a story. I think this need for a story is an example of fiction being more believable, and certainly more compelling, than simply telling the facts of what happened. The facts of what happened have their own place in my memory, but it takes a story to convey to others a sense of your own experience.
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Displaying 1 of 1 review
December 29, 2024
Fast but comprehensible conceptual overview
My background is working briefly in the 1960's with artificial neurons for pattern recognition. I read/scanned this in a day. Seemed like a very good overview of the concepts involved. I liked the code examples illuminating each concept. Would have to see some actual pictures of interconnections between networks of neurons. Would have liked to see where current tools like chatGPT were composed of the various types of neurons using the different.
My background is working briefly in the 1960's with artificial neurons for pattern recognition. I read/scanned this in a day. Seemed like a very good overview of the concepts involved. I liked the code examples illuminating each concept. Would have to see some actual pictures of interconnections between networks of neurons. Would have liked to see where current tools like chatGPT were composed of the various types of neurons using the different.
Displaying 1 of 1 review