Fixed typo

Author: hiromis

Lesson1.md

@@ -687,7 +687,7 @@ Uh-oh. The error got much worse. Why? In order to understand why, we are actuall
 [[1:16:28](https://youtu.be/BWWm4AzsdLk?t=4588)]
 ![](lesson1/100.png)
 
-These pictures come from [a fantastic paper](https://cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf) by Matt Zeiler who nowadays is a CEO of Clarify which is a very successful computer vision startup and his supervisor for his PhD Rob Fergus. They wrote a paper showing how you can visualize the layers of a convolutional neural network. A convolutional neural network, which we will learn mathematically about what the layers are shortly, but the basic idea is that your red, green, and blue pixel values that are numbers from nought to 255 go into the simple computation (i.e. the first layer) and something comes out of that, and then the result of that goes into a second layer, and the result of that goes into the third layer and so forth. There can be up to a thousand layers of neural network. ResNet34 has 34 layers, and ResNet50 has 59 layers, but let's look at layer one. There's this very simple computation which is a convolution if you know what they are. What comes out of this first layer? Well, we can actually visualize these specific coefficients, the specific parameters by drawing them as a picture. There's actually a few dozen of them in the first layer, so we don't draw all of them. Let's just look at 9 at random. 
+These pictures come from [a fantastic paper](https://cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf) by Matt Zeiler who nowadays is a CEO of Clarify which is a very successful computer vision startup and his supervisor for his PhD Rob Fergus. They wrote a paper showing how you can visualize the layers of a convolutional neural network. A convolutional neural network, which we will learn mathematically about what the layers are shortly, but the basic idea is that your red, green, and blue pixel values that are numbers from nought to 255 go into the simple computation (i.e. the first layer) and something comes out of that, and then the result of that goes into a second layer, and the result of that goes into the third layer and so forth. There can be up to a thousand layers of neural network. ResNet34 has 34 layers, and ResNet50 has 50 layers, but let's look at layer one. There's this very simple computation which is a convolution if you know what they are. What comes out of this first layer? Well, we can actually visualize these specific coefficients, the specific parameters by drawing them as a picture. There's actually a few dozen of them in the first layer, so we don't draw all of them. Let's just look at 9 at random.