Table of Contents Preface Tensors, Operations, Variables and Automatic Differentiation Tensors Operations Variables Automatic Differentiation Linear Regression AutoGraph AutoGraph Functions Linear Regression Revisited Caveats Models, Layers and Activations Models Layers Activations Fully Connected Networks Optimizers Gradient Descent Stochastic Gradient Descent Momentum Second Moment Loss Functions Please enable JavaScript to view the comments powered by Disqus.