Merge branch 'master' of https://github.com/haipinglu/ScalableML

haipinglu · haipinglu · commit 3a130486b88a · 2023-03-12T09:42:36.000Z
diff --git a/Lab 2 - RDD, DataFrame, ML pipeline, and parallelization.md b/Lab 2 - RDD, DataFrame, ML pipeline, and parallelization.md
@@ -39,7 +39,7 @@ Firstly, we follow the standard steps as in Task 2 of Lab 1 but with some variat
 
    ```sh
    qrshx -P rse-com6012 -pe smp 4 # request 4 CPU cores using our reserved queue
-   source myspark.sh # assuming HPC/myspark.sh is under the root directory, otherwise, see Lab 1 Task 2
+   source myspark.sh # assuming HPC/myspark.sh is under your root directory, otherwise, see Lab 1 Task 2
    conda install -y numpy # install numpy, to be used in Task 3. This ONLY needs to be done ONCE. NOT every time.
    cd com6012/ScalableML # our main working directory
    pyspark --master local[4] # start pyspark with 4 cores requested above.
diff --git a/Lab 3 - Scalable Logistic Regression.md b/Lab 3 - Scalable Logistic Regression.md
@@ -1,5 +1,9 @@
 # Lab 3: Scalable logistic regression
 
+### Modified by Shuo Zhou, 19th February 2023
+
+#### 25th February 2022 Mauricio A Álvarez
+
 ## Study schedule
 
 - [Section 1](#1-data-storage-in-sharc-and-spark-configuration): To finish by 24th February. **Essential**
diff --git a/Lab 4 - Scalable Generalized Linear Models.md b/Lab 4 - Scalable Generalized Linear Models.md
@@ -1,5 +1,9 @@
 # Lab 4: Scalable Generalized Linear Models
 
+### Modified by Shuo Zhou, 26th February 2023
+
+#### 28th February 2022 Mauricio A Álvarez
+
 ## Study schedule
 
 - [Section 1](#1-glms-in-pyspark): To finish by 3rd March. **Essential**
