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NEURAL NETWORKS AND DEEP LEARNING WITH PYTHON A PRACTICAL APPROACH
Neural Networks and Deep Learning with A Practical Approach
Embark on a journey through the powerful, interconnected world of Neural Networks and Deep Learning. This comprehensive guide explores foundational and advanced concepts, making it an important resource for beginners, students, and research scholars eager to understand the mechanics of neural networks and their impact across industries. This book bridges the gap between theory and real-world applications through structured explanations, mathematical formulas, hands-on Python code, and intuitive diagrams.Inside, you’ll
Essentials of Neural Networks: From perceptrons to multi-layer networks, uncover the core building blocks that define how neural networks learn and operate.
Deep Learning Explained: Go beyond the basics with an exploration of deep learning architectures like CNNs, RNNs, and GANs. See how these systems drive innovations in vision, natural language processing, and generative models.
Python Code and Practical Exercises: Designed for hands-on learning, the book includes clear, annotated Python code, along with practical examples to reinforce understanding and give you the tools to start creating your own models.
Advanced Topics and Mathematical Foundations: Gain a deeper appreciation of the complex mathematics underpinning neural networks. With step-by-step explanations of formulas and concepts, you’ll master topics such as backpropagation, gradient descent, and activation functions.
Diagrams and Visual Aids: To ensure complex ideas are easily grasped, each section is enhanced with visually engaging diagrams that make concepts intuitive and relatable.
Whether you're a beginner hoping to understand the basics, or an advanced reader looking to explore the intricacies of neural network architecture, this book is your key to mastering Neural Networks and Deep Learning.
Join the thousands already captivated by the potential of deep learning and discover the power of neural networks with Python!
Embark on a journey through the powerful, interconnected world of Neural Networks and Deep Learning. This comprehensive guide explores foundational and advanced concepts, making it an important resource for beginners, students, and research scholars eager to understand the mechanics of neural networks and their impact across industries. This book bridges the gap between theory and real-world applications through structured explanations, mathematical formulas, hands-on Python code, and intuitive diagrams.Inside, you’ll
Essentials of Neural Networks: From perceptrons to multi-layer networks, uncover the core building blocks that define how neural networks learn and operate.
Deep Learning Explained: Go beyond the basics with an exploration of deep learning architectures like CNNs, RNNs, and GANs. See how these systems drive innovations in vision, natural language processing, and generative models.
Python Code and Practical Exercises: Designed for hands-on learning, the book includes clear, annotated Python code, along with practical examples to reinforce understanding and give you the tools to start creating your own models.
Advanced Topics and Mathematical Foundations: Gain a deeper appreciation of the complex mathematics underpinning neural networks. With step-by-step explanations of formulas and concepts, you’ll master topics such as backpropagation, gradient descent, and activation functions.
Diagrams and Visual Aids: To ensure complex ideas are easily grasped, each section is enhanced with visually engaging diagrams that make concepts intuitive and relatable.
Whether you're a beginner hoping to understand the basics, or an advanced reader looking to explore the intricacies of neural network architecture, this book is your key to mastering Neural Networks and Deep Learning.
Join the thousands already captivated by the potential of deep learning and discover the power of neural networks with Python!
535 pages, Paperback
Published November 17, 2024
About the author
Emmimal P. Alexander
2 booksEmmimal Alexander is an author, researcher, and educator with a strong focus on artificial intelligence, neural networks, and creative applications of technology. Her work bridges theory and practice, offering research scholars and professionals clear explanations, detailed diagrams, and hands-on Python examples to better understand the evolving world of AI.
She also shares tutorials, articles, and practical insights on her website emitechlogic.com, where she explores the future of AI, data visualization, and innovative approaches to problem-solving. Known for her approachable style, she aims to inspire readers to learn, create, and apply AI in ways that make a real impact.
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