A classic way of minimizing a fitness function (
) is to repeatedly follow the gradient in the direction of steepest descent. The gradient function 
is defined as the vector of the partial differentials of 
, which is denoted as follows:
- Title Page
- Copyright and Credits
- Dedication
- Packt Upsell
- Contributors
- Preface
- A Quick Refresher
- Building Our First Neural Network Together
- Decision Trees and Random Forests
- Face and Motion Detection
- Training CNNs Using ConvNetSharp
- Training Autoencoders Using RNNSharp
- Replacing Back Propagation with PSO
- Function Optimizations: How and Why
- Technical requirements
- Getting started
- Function minimization and maximization
- Hyperparameters and tuning
- Understanding visualizations
- Plotting results
- Adding new optimization functions
- Summary
- Finding Optimal Parameters
- Object Detection with TensorFlowSharp
- Time Series Prediction and LSTM Using CNTK
- GRUs Compared to LSTMs, RNNs, and Feedforward networks
- Activation Function Timings
- Function Optimization Reference
- The Currin Exponential function
- The Webster function
- The Oakley & O'Hagan function
- The Grammacy function
- Franke's function
- The Lim function
- The Ackley function
- The Bukin function N6
- The Cross-In-Tray function
- The Drop-Wave function
- The Eggholder function
- The Holder Table function
- The Levy function
- The Levy function N13
- The Rastrigin function
- The Schaffer function N.2
- The Schaffer function N.4
- The Shubert function
- The Rotated Hyper-Ellipsoid function
- The Sum Squares function
- The Booth function
- The Mccormick function
- The Power Sum function
- The Three-Hump Camel function
- The Easom function
- The Michalewicz function
- The Beale function
- The Goldstein-Price function
- The Perm function
- The Griewank function
- The Bohachevsky function
- The Sphere function
- The Rosenbrock function
- The Styblinski-Tang function
- Summary
- Keep reading
- Other Books You May Enjoy
Gradient descent (GD)
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