By Satish Kumar.pdf — Neural Networks A Classroom Approach
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"Neural Networks: A Classroom Approach" by Satish Kumar provides a foundational overview of artificial neural networks, blending biological, mathematical, and geometric perspectives. It covers key concepts like feedforward and recurrent networks, backpropagation, and SVMs, with practical insights through MATLAB simulations. For more details, visit McGraw Hill Neural Networks- A Classroom Approach - McGraw Hill
For unsupervised learning, the book details Kohonen’s Self-Organizing Maps. It explains how high-dimensional data can be mapped onto low-dimensional (usually 2D) grids while preserving the topological properties of the input space. Target Audience This book is ideal for several groups of learners: Neural Networks A Classroom Approach By Satish Kumar.pdf
: Covers Statistical Learning Theory, Support Vector Machines (SVMs) , and Radial Basis Function (RBF) networks to address non-linear dependencies. Pedagogical Features Neural Networks: A Classroom Approach | PDF | Deep Learning
The book also includes appendices that provide essential mathematical background, ensuring that the main text can focus on conceptual clarity. For more details, visit McGraw Hill Neural Networks-
: Details specific learning rules such as: Hebbian Learning : Adjusting weights based on node activity.
On the other hand, some readers find the book challenging, for the very same reasons. A critical review suggests that the book tends to "overcomplicate simple things" and goes "too mathematical right from the start". The same reviewer explicitly states that the book is with no prior experience in learning algorithms or a strong mathematics background. This reviewer also notes that the content can feel "rather primitive" when compared to more modern books focused on deep learning. Pedagogical Features Neural Networks: A Classroom Approach |
Published by McGraw-Hill Education and written specifically for the academic environment, this book is intended for senior undergraduate and graduate students in engineering, particularly those in their first course on neural networks. "Neural Networks: A Classroom Approach" assumes a basic understanding of mathematics and computer programming, blending these foundational areas to explore the diversity of neural network models. The target audience includes students of electrical engineering, computer science, physics, and anyone with a quantitative background looking to delve into machine learning and soft computing.
This final part distinguishes the book by covering topics often left for more advanced volumes. It includes chapters on Support Vector Machines (SVM) and Statistical Learning Theory, Fuzzy Systems, Pulsed Neural Networks (a nod to more biologically realistic models), and a final chapter on Soft Computing and Dynamical Systems, which ties many of the concepts together.
