Skip to content
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion notebooks/chapter19/Optimizer and Backpropagation.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
"\n",
"## Stochastic Gradient Descent\n",
"\n",
"The goal of an optimization algorithm is to nd the value of the parameter to make loss function very low. For some types of models, an optimization algorithm might find the global minimum value of loss function, but for neural network, the most efficient way to converge loss function to a local minimum is to minimize loss function according to each example.\n",
"The goal of an optimization algorithm is to find the value of the parameter to make loss function very low. For some types of models, an optimization algorithm might find the global minimum value of loss function, but for neural network, the most efficient way to converge loss function to a local minimum is to minimize loss function according to each example.\n",
"\n",
"Gradient descent uses the following update rule to minimize loss function:"
]
Expand Down