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Fixed typo #17

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Fixed typo #17

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2 changes: 1 addition & 1 deletion tutorial_deep_learning_basics/deep_learning_basics.ipynb
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Expand Up @@ -46,7 +46,7 @@
"\n",
"![Deep learning concepts](https://i.imgur.com/EAl47rp.png)\n",
"\n",
"At a high-level, neural networks are either encoders, decoders, or a combination of both. Encoders find patterns in raw data to form compact, useful representations. Decoders generate new data or high-resolution useful infomation from those representations. As the lecture describes, deep learning discovers ways to **represent** the world so that we can reason about it. The rest is clever methods that help use deal effectively with visual information, language, sound (#1-6) and even act in a world based on this information and occasional rewards (#7).\n",
"At a high-level, neural networks are either encoders, decoders, or a combination of both. Encoders find patterns in raw data to form compact, useful representations. Decoders generate new data or high-resolution useful infomation from those representations. As the lecture describes, deep learning discovers ways to **represent** the world so that we can reason about it. The rest is clever methods that help us deal effectively with visual information, language, sound (#1-6) and even act in a world based on this information and occasional rewards (#7).\n",
"\n",
"1. **Feed Forward Neural Networks (FFNNs)** - classification and regression based on features. See [Part 1](#Part-1:-Boston-Housing-Price-Prediction-with-Feed-Forward-Neural-Networks) of this tutorial for an example.\n",
"2. **Convolutional Neural Networks (CNNs)** - image classification, object detection, video action recognition, etc. See [Part 2](#Part-2:-Classification-of-MNIST-Dreams-with-Convolution-Neural-Networks) of this tutorial for an example.\n",
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