ENH: Add GitHub Action based Link checker (#217)

* Enable Link Checker and Report Uploading

* Enable action for pull requests

* Fix typo for running workflow

* enable build cache

* update broken links

Co-authored-by: Humphrey Yang <u6474961@anu.edu.au>

Author: mmcky

.github/workflows/linkcheck.yml

@@ -0,0 +1,44 @@
+name: Link Checker [Anaconda, Linux]
+on:
+  pull_request:
+    types: [opened, reopened]
+  schedule:
+    # UTC 12:00 is early morning in Australia
+    - cron:  '0 12 * * *'
+jobs:
+  execution-tests-linux:
+    name: Execution Tests (${{ matrix.python-version }}, ${{ matrix.os }})
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: ["ubuntu-latest"]
+        python-version: ["3.9"]
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+      - name: Setup Anaconda
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          auto-update-conda: true
+          auto-activate-base: true
+          miniconda-version: 'latest'
+          python-version: 3.9
+          environment-file: environment.yml
+          activate-environment: quantecon
+      - name: Download "build" folder (cache)
+        uses: dawidd6/action-download-artifact@v2
+        with:
+          workflow: cache.yml
+          branch: main
+          name: build-cache
+          path: _build
+      - name: Link Checker
+        shell: bash -l {0}
+        run: jb build lectures --path-output=./ --builder=custom --custom-builder=linkcheck
+      - name: Upload Link Checker Reports
+        uses: actions/upload-artifact@v2
+        if: failure()
+        with:
+          name: linkcheck-reports
+          path: _build/linkcheck

lectures/about_py.md

@@ -474,7 +474,7 @@ Apart from the cloud computing options listed above, you might like to consider
 ```{index} single: parallel computing; Dask
 ```
 
-* [Dask](https://www.dask.org/) parallelises PyData and Machine Learning in Python.
+* [Dask](https://docs.dask.org/en/stable/) parallelises PyData and Machine Learning in Python.
 
 ```{index} single: parallel computing; pycuda
 ```

lectures/parallelization.md

@@ -342,7 +342,7 @@ wealth of the group at the end of a long simulation.
 Moreover, provided the simulation period is long enough, initial conditions
 don't matter.
 
-* This is due to something called ergodicity, which we will discuss [later on](https://python-intro.quantecon.org/finite_markov.html#Ergodicity).
+* This is due to something called ergodicity, which we will discuss [later on](https://python.quantecon.org/finite_markov.html#id15).
 
 So, in summary, we are going to simulate 50,000 households by