Overview

Introduction to Neural Networks is a clear, hands-on guide that takes you from decision trees to fully functional neural networks. Written by Brega Joel Othniel and Drogba Ketoura under the supervision of Dr. Cyr Emile M'Lan, the book blends theory with real-world case studies you can reproduce today. Learn the core building blocks-neurons, layers, weights, biases, and activation functions (Sigmoid, Tanh, ReLU)-through intuitive explanations and LaTeX equations. Master training mechanics: forward/backward passes, cross-entropy and MSE loss, gradient descent, regularization, and early stopping. Five complete applications show the power of neural networks in action: LSTM-based predictive irrigation that cuts water use by 20-46 % while preserving crop yield. Hard-drive failure forecasting using SMART data and regression models. Mobile-banking adoption analysis in Bangladesh with sensitivity-ranked factors. House-price prediction in Singapore outperforming multiple regression (R² ≈ 0.966). Next-day AAPL stock closing price forecast (MAE $3.64, R² 0.985) using only five daily inputs. All examples include R code (quantmod, neuralnet, NeuralNetTools), datasets (Iris, AAPL 2020-2024), detailed figures, tables, and performance metrics. Whether you are a student, researcher, farmer, data engineer, or financial analyst, this book equips you to build, understand, and deploy neural networks that solve real problems.

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Brega Joel OthnielBrega Joel Othniel earned a Bachelor's degree in Mathematics with a concentration in Financial Engineering, where he developed a deep expertise in quantitative modeling, risk analysis, and computational finance. Driven by a passion for data-driven finance, he specializes in using machine learning to uncover hidden patterns in financial markets-turning raw numbers into predictive insights that inform investment strategies, risk management, and algorithmic trading. His interest in neural networks emerged as a powerful tool to model the non-linear, chaotic behavior of financial systems and real-world phenomena alike. With hands-on experience in R, Python, and time-series forecasting, Joel builds models that don't just predict-they explain. From forecasting AAPL stock movements with over 98 % accuracy to designing irrigation systems that save water in sub-Saharan farms, his work bridges theory and impact. Now stepping boldly into technical authorship, Joel uses writing as a platform to democratize advanced AI. Introduction to Neural Networks is his first book-a clear, code-first guide that equips readers to train their own models in minutes. This project reflects his core belief: complex tools should be accessible to all.

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