Lab 4 update

spark-mooc · Aug 3, 2016 · 8252845 · 8252845
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diff --git a/cs120_autograder_complete.dbc b/cs120_autograder_complete.dbc
diff --git a/cs120_autograder_register.dbc b/cs120_autograder_register.dbc
diff --git a/cs120_autograder_simpler.dbc b/cs120_autograder_simpler.dbc
diff --git a/cs120_lab0.dbc b/cs120_lab0.dbc
diff --git a/cs120_lab1a_math_review.dbc b/cs120_lab1a_math_review.dbc
diff --git a/cs120_lab1b_word_count_rdd.dbc b/cs120_lab1b_word_count_rdd.dbc
diff --git a/cs120_lab2_linear_regression_df.dbc b/cs120_lab2_linear_regression_df.dbc
diff --git a/cs120_lab3_ctr_df.dbc b/cs120_lab3_ctr_df.dbc
diff --git a/cs120_lab4_pca.dbc b/cs120_lab4_pca.dbc
diff --git a/cs120_lab4_pca.py b/cs120_lab4_pca.py
@@ -9,7 +9,7 @@
 # MAGIC ![ML Logo](http://spark-mooc.github.io/web-assets/images/CS190.1x_Banner_300.png)
 # MAGIC # Principal Component Analysis Lab
 # MAGIC 
-# MAGIC This lab delves into exploratory analysis of neuroscience data, specifically using principal component analysis (PCA) and feature-based aggregation. We will use a dataset of light-sheet imaging recorded by the [Ahrens Lab](http://www.janelia.org/lab/ahrens-lab) at Janelia Research Campus, and hosted on the CodeNeuro [data repository](http://datasets.codeneuro.org).
+# MAGIC This lab delves into exploratory analysis of neuroscience data, specifically using principal component analysis (PCA) and feature-based aggregation. We will use a dataset of light-sheet imaging recorded by the [Ahrens Lab](http://www.janelia.org/lab/ahrens-lab) at Janelia Research Campus.
 # MAGIC 
 # MAGIC Our dataset is generated by studying the movement of a larval [zebrafish](http://en.wikipedia.org/wiki/Zebrafish), an animal that is especially useful in neuroscience because it is transparent, making it possible to record activity over its entire brain using a technique called [light-sheet microscopy](http://en.wikipedia.org/wiki/Light_sheet_fluorescence_microscopy).   Specifically, we'll work with time-varying images containing patterns of the zebrafish's neural activity as it is presented with a moving visual pattern.   Different stimuli induce different patterns across the brain, and we can use exploratory analyses to identify these patterns.  Read ["Mapping brain activity at scale with cluster computing"](http://thefreemanlab.com/work/papers/freeman-2014-nature-methods.pdf) for more information about these kinds of analyses.
 # MAGIC