Merge pull request #1 from dancodery/patch-1

Patch 1
stephencwelch · Jan 17, 2019 · 16dae4f · 16dae4f
2 parents feb8e82 + cd74a0b
commit 16dae4f
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diff --git a/notebooks/Self-Driving Cars [Part 1- The ALV].ipynb b/notebooks/Self-Driving Cars [Part 1- The ALV].ipynb
@@ -610,7 +610,7 @@
     }
    ],
    "source": [
-    "interact(apply_alv_vision, t = (40, 130), thresh = (-64, 64))"
+    "interact(apply_alv_vision, t = (0, 10), thresh = (-64, 64))"
    ]
   },
   {

diff --git a/notebooks/Self-Driving Cars [Part 2 - ALVINN].ipynb b/notebooks/Self-Driving Cars [Part 2 - ALVINN].ipynb
@@ -672,7 +672,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- Our network has learned to intrepret driving images based ONLY on steering angle! \n",
+    "- Our network has learned to interpret driving images based ONLY on steering angle! \n",
     "- For this data (track data with only left turns), our network has learned a set of lane marker detectors!\n"
    ]
   },
@@ -688,7 +688,7 @@
    "metadata": {},
    "source": [
     "- For a number of years from the late 1980s to the early 1990s, ALVINN was the most successfull vision algorithm for autonomous driving at CMU, and possibly on the planet.\n",
-    "- Faster computers enabled faster driving, and more sophisiticed training and data augmentation procedures allowed ALVINN to learn quickly and effectively across many environments."
+    "- Faster computers enabled faster driving, and more sophisticated training and data augmentation procedures allowed ALVINN to learn quickly and effectively across many environments."
    ]
   },
   {
@@ -703,7 +703,7 @@
    "metadata": {},
    "source": [
     "- And quite recently, Nvidia implemented Pomerleau's idea using **Deep Neural Networks**\n",
-    "- Great writeup in thier publication [End to end learning for self-driving cars](https://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf)\n",
+    "- Great writeup in their publication [End to end learning for self-driving cars](https://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf)\n",
     "- Pomerleau orinally trained on ~5 minutes of driving, Nvidia trained on 3000 hours\n",
     "    - That's 36,000 times the amount of data - **5 orders of magnitude** increase in dataset size in 30 years. \n"
    ]