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--- intern:construction_har [2022/08/12 02:37] – kenzjk
+++ intern:construction_har [2022/08/13 10:28] (current) – kenzjk
@@ Line 23: / Line 23: @@
 __Aug/08/22__
   * Studied // Human Activity Recognition using Wearable Sensors by Deep Convolutional Neural Networks // and discussed this paper with my professor.
-  * Did research on some common libraries implemented by machine learning programs, such as SciKit-Learn, NumPy, MatPlotLib, etc.
+  * Did research and watched lecture videos on common libraries implemented by machine learning programs, such as SciKit-Learn (https://www.youtube.com/watch?v=0Lt9w-BxKFQ&t=904s), NumPy (https://www.youtube.com/watch?v=zoLHuhefMNw), MatPlotLib (https://www.youtube.com/watch?v=3Xc3CA655Y4), etc.
 __Aug/10/22__
   * Coded an iris flower-distinguishing program using Sklearn.
   * Began to code and train a standing-or-walking-distinguishing program as a first step to our final program to be implemented at construction sites.
 __Aug/12/22__
-  * Continued training the HAR program using the UCI HAR Dataset.
+  * Successfully read in all the data from the UCI HAR Dataset (https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones) into my machine learning program.
   * Studied an open source HAR algorithm on GitHub (https://github.com/ma-shamshiri/Human-Activity-Recognition/tree/main/code) to familiarize myself with typical HAR programs.
 __Aug/14/22__
+  * Successfully trained my program using the UCI HAR Dataset, but prediction accuracy was not ideal.
+  * Experimented with common HAR classifiers seen on GitHub to try to find better prediction accuracy.
 ==== 5. Final Report PPT ====