Introduction to Neural Networks

<p><em>Introduction to Neural Networks</em> 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.</p><p>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.</p><p>Five complete applications show the power of neural networks in action:</p><ul><li><strong>LSTM-based predictive irrigation</strong> that cuts water use by 20-46 % while preserving crop yield.</li><li><strong>Hard-drive failure forecasting</strong> using SMART data and regression models.</li><li><strong>Mobile-banking adoption analysis</strong> in Bangladesh with sensitivity-ranked factors.</li><li><strong>House-price prediction</strong> in Singapore outperforming multiple regression (R² ≈ 0.966).</li><li><strong>Next-day AAPL stock closing price</strong> forecast (MAE $3.64 R² 0.985) using only five daily inputs.</li></ul><p>All examples include <strong>R code</strong> (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.</p>