Tutorial 8: Fixing typo

docs/tutorial_notebooks/tutorial8/Deep_Energy_Models.ipynb

@@ -140,7 +140,7 @@
     "1. The probability distribution needs to assign any possible value of $\\mathbf{x}$ a non-negative value: $p(\\mathbf{x}) \\geq 0$.\n",
     "2. The probability density must sum/integrate to 1 over **all** possible inputs: $\\int_{\\mathbf{x}} p(\\mathbf{x}) d\\mathbf{x} = 1$. \n",
     "\n",
-    "Luckily, there are actually many approaches for this, and one of them are energy-based models. The fundamental idea of energy-based models is that you can turn any function that predicts values larger than zero into a probability distribution by dviding by its volume. Imagine we have a neural network, which has as output a single neuron, like in regression. We can call this network $E_{\\theta}(\\mathbf{x})$, where $\\theta$ are our parameters of the network, and $\\mathbf{x}$ the input data (e.g. an image). The output of $E_{\\theta}$ is a scalar value between $-\\infty$ and $\\infty$. Now, we can use basic probability theory to *normalize* the scores of all possible inputs:\n",
+    "Luckily, there are actually many approaches for this, and one of them are energy-based models. The fundamental idea of energy-based models is that you can turn any function that predicts values larger than zero into a probability distribution by dividing by its volume. Imagine we have a neural network, which has as output a single neuron, like in regression. We can call this network $E_{\\theta}(\\mathbf{x})$, where $\\theta$ are our parameters of the network, and $\\mathbf{x}$ the input data (e.g. an image). The output of $E_{\\theta}$ is a scalar value between $-\\infty$ and $\\infty$. Now, we can use basic probability theory to *normalize* the scores of all possible inputs:\n",
     "\n",
     "$$\n",
     "q_{\\theta}(\\mathbf{x}) = \\frac{\\exp\\left(-E_{\\theta}(\\mathbf{x})\\right)}{Z_{\\theta}} \\hspace{5mm}\\text{where}\\hspace{5mm}\n",