by Benjamin Midtvedt, Jesús Pineda, Henrik Klein Moberg, Harshith Bachimanchi, Joana B. Pereira, Carlo Manzo, Giovanni Volpe
No Starch Press, San Francisco (CA), 2025
ISBN-13: 9781718503922
https://nostarch.com/deep-learning-crash-course
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Dense Neural Networks for Regression
Explores regression problems and digital twins, focusing on continuous-value prediction with multi-layer networks.
Code 2-1: Regressing 1D Data with a Single Neuron
Implements a single neuron to perform linear regression on 1D data. The example introduces weight updates using stochastic gradient descent to minimize prediction errors.Code 2-2: Regressing 2D Data with a Single Neuron
Extends the single-neuron approach to multidimensional data, demonstrating the scalar product between input and weights to predict outputs in 2D data.Code 2-3: Regressing 2D Data with a Two-Layer Neural Network
Explores two-layer dense neural networks to model linear functions in 2D data using backpropagation for weight updates and training.Code 2-4: Fitting Data with a Neural Network Trained Using Batch Training
Demonstrates mini-batch training for 2D regression tasks, incorporating batch randomization to improve stability and avoid local minima.Code 2-5: Training a Neural Network Splitting the Data
Divides data into training, validation, and test sets to train and validate a two-layer network, monitoring metrics to identify overfitting.Code 2-A: Simulating the Forces Acting on an Optically Trapped Particle
Uses a dense neural network to emulate optical forces on a microsphere in optical tweezers, creating a digital twin to model real-world systems efficiently.