updates

Author: msneubauer

_sources/homework/Homework_06.ipynb

@@ -46,17 +46,17 @@
    "id": "3c77299b",
    "metadata": {},
    "source": [
-    "Simulations of the motion of several trapezoid shaped projectiles after having been catapulted from the ground were performed using [notebook](https://github.com/GDS-Education-Community-of-Practice/DSECOP/blob/main/Time_Series_Analysis_and_Forecasting/01_Simulating_Projectile_Motion_with_Drag.ipynb) from the APS Group on Data Science. This is an imagined scenario but takes the simple Newtonian motion of an idealized projectile and considers a more realistic scenario of varied drag.\n",
+    "Simulations of the motion of several trapezoid shaped projectiles after having been catapulted from the ground were performed using [this repo](https://github.com/GDS-Education-Community-of-Practice/DSECOP/blob/main/Time_Series_Analysis_and_Forecasting) from the APS Group on Data Science. This is an imagined scenario but takes the simple Newtonian motion of an idealized projectile and considers a more realistic scenario of varied drag.\n",
     "\n",
     "This data includes a varied drag coefficient and projectile area for four sides of the object.\n",
     "\n",
     "A large number of these runs was simulated and saved in the file `launches.csv`. We begin by loading this file of simulated launches.\n",
     "\n",
-    "In [notebook](https://github.com/GDS-Education-Community-of-Practice/DSECOP/blob/main/Time_Series_Analysis_and_Forecasting/02_Time_Series_Analysis_and_Forecasting.ipynb), some classical time series analysis techniques to better understand that data and then demonstrated linear techniques for \"forecasting\" or predicting the future state of the projectile, given some initial portion of the data.\n",
+    "Some classical time series analysis techniques to better understand that data and then demonstrated linear techniques for \"forecasting\" or predicting the future state of the projectile, given some initial portion of the data, is provided in the APS GDS repository. \n",
     "\n",
     "In this notebook, we will explore the use of <span style=\"color:Violet\">neural networks</span>, which as you know are nonlinear models, to forecast future states of the projectile, given the previous locations and other information. For example, if we know how the projectile travelled from time $t=0$ to time $t=10$, where will it be at time $t=11$?\n",
     "\n",
-    "Although you can calculate this with Newton's second law, the previously referenced [notebook](https://github.com/GDS-Education-Community-of-Practice/DSECOP/blob/main/Time_Series_Analysis_and_Forecasting/02_Time_Series_Analysis_and_Forecasting.ipynb) demonstrated that this calculation can be more tricky if you do not know the exact drag coefficient on the projectile.\n",
+    "Although you can calculate this with Newton's second law, this calculation can be more tricky if you do not know the exact drag coefficient on the projectile.\n",
     "\n",
     "We begin by loading the file of simulated launches, which will be our data for tuning and testing the neural network parameters."
    ]