Add/fix Colab badges to tutorials for easier access

docs/curriculum/api-usage/honegumi-api-getting-started.ipynb

@@ -6,14 +6,9 @@
     "id": "DYoVMfsLfE57"
    },
    "source": [
-    "# Getting Started with the Honegumi API"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a href=\"https://colab.research.google.com/github/sgbaird/honegumi/blob/main/docs/curriculum/api-usage/honegumi-api-getting-started.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "# Getting Started with the Honegumi API\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sgbaird/honegumi/blob/main/docs/curriculum/api-usage/honegumi-api-getting-started.ipynb)"
    ]
   },
   {

docs/curriculum/tutorials/benchmarking/benchmarking.ipynb

@@ -6,6 +6,8 @@
    "source": [
     "# Benchmarking Acquisition Functions\n",
     "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sgbaird/honegumi/blob/main/docs/curriculum/tutorials/benchmarking/benchmarking.ipynb)\n",
+    "\n",
     "The choice of acquisition function for a Bayesian optimization campaign is an important decision that can greatly affect the efficiency with which an optimal solution is found. While expected improvement is often a good starting choice, it isn't always the most effective acquisition function. For example, upper confidence bound is known to outperform expected improvement in multimodal problems where exploration is essential to finding the global optima. However, knowing which acquisition function to choose for a given problem isn't always straightforward, especially for complicated design spaces.\n",
     "\n",
     "One approach is to simulate optimization campaigns with different acquisition functions on synthetic problems. By directly controlling the parameters of the problem, practitioners can benchmark different optimization setups ahead of a real experimental campaign. This can then guide practitioners towards better optimization strategies - provided the synthetic optimization problem is sufficienty similar to the problem of interest. Fortunately, there are a wide variety of synthetic functions available within the [Ax library](https://ax.dev/api/metrics.html) and [online](https://www.sfu.ca/~ssurjano/optimization.html) that can be used.\n",

docs/curriculum/tutorials/featurization/featurization.ipynb

@@ -6,6 +6,8 @@
    "source": [
     "# Optimizing MAX Phases with Featurization\n",
     "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sgbaird/honegumi/blob/main/docs/curriculum/tutorials/featurization/featurization.ipynb)\n",
+    "\n",
     "Some design spaces cannot be reduced to simple, continuous representations that can be fed into Ax. For example, material compositions often span the periodic table and are subject to non-linear constraints like parsimony and electron counting rules that would be impossible to express in an Ax parameters object. One possible solution is to represent the composition as a one dimensional vector of the molar fractions of the materials' constituent elements. However, this representation assumes simple elemental substitution rules, which tyically only hold in limited composition ranges. Additionally, such a representation provides the model with little information about the unerlying physics and has been shown to be a weak predictor of material properties.\n",
     "\n",
     "Featurization is the process of creating new representations that better describe the input data and are more ammendable to statistical modeling. In the context of material compositions, featurization typically involves creating a weighted combination of teh constituent elements' atomic properties. In this scheme, a material like Al2O3 is represented as a vector by averaging the properties of aluminum and oxygen, weighted by their molar fractions. In this tutorial, the popular featurization package,[CBFV](https://pypi.org/project/CBFV/), will be used to perform the featurization task.\n",

docs/curriculum/tutorials/mobo/mobo.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Multi Objective Optimization of Polymers for Strength and Biodegradability\n",
     "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sgbaird/honegumi/blob/main/docs/curriculum/tutorials/mobo/mobo-tutorial.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sgbaird/honegumi/blob/main/docs/curriculum/tutorials/mobo/mobo.ipynb)"
    ]
   },
   {

docs/curriculum/tutorials/multitask/multitask.ipynb

@@ -6,6 +6,8 @@
    "source": [
     "# Multi-Task Optimization Across Ceramic Binder Systems\n",
     "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sgbaird/honegumi/blob/main/docs/curriculum/tutorials/multitask/multitask.ipynb)\n",
+    "\n",
     "Tape casting is a manufacturing technique for producing thin, uniform ceramic components that find applications in fuel cells, batteries, and capacitors. The fabrication process involves casting a thin ceramic slurry over a flat sheet using a doctor blade, a drying process, and then debinding and sintering.\n",
     "\n",
     "You are interesting in fabricating casting some thin, but robust ceramic membranes for use in a hydrogen fuel cell. Green strength appears to be an important factor so you decide to target this metric. After working through the literature you identify the following key slurry components and the likely ranges.\n",

docs/curriculum/tutorials/sobo/sobo.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Single Objective Optimization of 3D Printed Materials\n",
     "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sgbaird/honegumi/blob/main/docs/curriculum/tutorials/sobo/sobo-tutorial.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sgbaird/honegumi/blob/main/docs/curriculum/tutorials/sobo/sobo.ipynb)"
    ]
   },
   {