Introduction to Neural Networks

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.