removing todos as part of competition

Author: avaamini

lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb

@@ -248,9 +248,8 @@
         "  loss=tf.keras.losses.MeanSquaredError(), # MSE loss for the regression task\n",
         ")\n",
         "\n",
-        "# TODO: Train the model for 30 epochs. Use model.fit().\n",
-        "loss_history = dense_NN.fit(x_train, y_train, epochs=30) \n",
-        "# loss_history = # TODO"
+        "# Train the model for 30 epochs using model.fit().\n",
+        "loss_history = dense_NN.fit(x_train, y_train, epochs=30)"
       ]
     },
     {
@@ -789,44 +788,7 @@
         "\n",
         "You've just analyzed the bias, aleatoric uncertainty, and epistemic uncertainty for your first risk-aware model! This is a task that data scientists do constantly to determine methods of improving their models and datasets.\n",
         "\n",
-        "## NOTE TO ADDRESS: THIS CAN BE ELIMINATED COMPLETELY IF IT IS TOO MUCH FOR COMPETITION!\n",
-        "### 1.6.1 Submission information\n",
-        "To be eligible for the Debiasing Faces Lab prize, you must submit a document of your answers to the short-answer `TODO`s with your complete lab submission. **Name your file in the following format: `[FirstName]_[LastName]_Debiasing_Report.pdf`.**\n",
-        "\n",
-        "Upload your document write-up as part of your complete lab submission for the Debiasing Faces Lab ([submission upload link](https://www.dropbox.com/request/TTYz3Ikx5wIgOITmm5i2)).\n",
-        "\n",
-        "Please see the short-answer `TODO`s replicated again here:\n",
-        "\n",
-        "#### **TODO: Inspecting the 2D regression dataset**\n",
-        "\n",
-        "1. What are your observations about where the train data and test data lie relative to each other?\n",
-        "2. What, if any, areas do you expect to have high/low aleatoric (data) uncertainty?\n",
-        "3. What, if any, areas do you expect to have high/low epistemic (model) uncertainty?\n",
-        "\n",
-        "#### **TODO: Analyzing the performance of standard regression model**\n",
-        "\n",
-        "1. Where does the model perform well?\n",
-        "2. Where does the model perform poorly?\n",
-        "\n",
-        "#### **TODO: Evaluating bias**\n",
-        "\n",
-        "1. How does the bias score relate to the train/test data density from the first plot?\n",
-        "2. What is one limitation of the Histogram approach that simply bins the data based on frequency?\n",
-        "\n",
-        "#### **TODO: Estimating aleatoric uncertainty**\n",
-        "\n",
-        "1. For what values of $x$ is the aleatoric uncertainty high or increasing suddenly?\n",
-        "2. How does your answer in (1) relate to how the $x$ values are distributed?\n",
-        "\n",
-        "#### **TODO: Estimating epistemic uncertainty**\n",
-        "\n",
-        "1. For what values of $x$ is the epistemic uncertainty high or increasing suddenly?\n",
-        "2. How does your answer in (1) relate to how the $x$ values are distributed (refer back to original plot)? Think about both the train and test data.\n",
-        "3. How could you reduce the epistemic uncertainty in regions where it is high?\n",
-        "\n",
-        "### 1.6.2 Moving forward\n",
-        "\n",
-        "In the next part of the lab, you'll continue to build off of these concepts to *mitigate* these risks, in addition to diagnosing them!\n",
+        "In the next part of the lab, you'll continue to build off of these concepts to study them in the context of facial detection systems: not only diagnosing issues of bias and uncertainty, but also developing solutions to *mitigate* these risks.\n",
         "\n",
         "![alt text](https://raw.githubusercontent.com/aamini/introtodeeplearning/2023/lab3/img/solutions_toy.png)"
       ]