1 00:00:00,266 --> 00:00:03,933 Hello and welcome to this section on Convolutional Neural Networks. 2 00:00:04,033 --> 00:00:07,333 Super excited that you're joining us for this section as well. 3 00:00:07,600 --> 00:00:11,000 And today we're going to cover off the plan of attack. 4 00:00:11,033 --> 00:00:13,566 How are we going to learn everything in this section? 5 00:00:13,566 --> 00:00:15,000 There's so much to learn. 6 00:00:15,000 --> 00:00:17,366 let's see how are we going to approach this. 7 00:00:17,366 --> 00:00:19,500 All right. What do we learn in this section? 8 00:00:19,500 --> 00:00:23,333 First of all, we'll talk about what convolutional networks actually are 9 00:00:23,333 --> 00:00:27,066 very important to understand the end goal that you're working towards 10 00:00:27,066 --> 00:00:28,700 before you actually start working towards it. 11 00:00:28,700 --> 00:00:30,266 So what about features? 12 00:00:30,266 --> 00:00:32,000 We'll have a look at a few little examples. 13 00:00:32,000 --> 00:00:35,000 We'll compare the human brain to artificial neural networks 14 00:00:35,000 --> 00:00:36,633 in terms of image recognition. 15 00:00:36,633 --> 00:00:42,000 So it'll be a fun a light tutorial to get us started for this whole section. 16 00:00:42,466 --> 00:00:47,333 Then we'll talk about step one diving straight into it convolution operation. 17 00:00:47,333 --> 00:00:52,300 So this part of the course contains several steps that we need to go through 18 00:00:52,300 --> 00:00:56,966 in order to build, a convolutional neural network. 19 00:00:56,966 --> 00:00:58,966 And that's how these tutorials are going to be broken up. 20 00:00:58,966 --> 00:01:00,433 So this one is going to be step one. 21 00:01:00,433 --> 00:01:06,400 The convolution operation will learn everything about feature detectors. 22 00:01:06,400 --> 00:01:08,700 We'll talk about which are also filters. 23 00:01:08,700 --> 00:01:10,900 We'll talk about feature maps. 24 00:01:10,900 --> 00:01:14,466 And you know, how what are the different parameters there, what they mean. 25 00:01:14,466 --> 00:01:17,400 And have a look at some visual examples as well. 26 00:01:17,400 --> 00:01:22,633 Then we'll talk about step one, part B, the ReLU layer or ReLU layer 27 00:01:22,933 --> 00:01:26,600 which is the rectified linear unit. 28 00:01:26,600 --> 00:01:30,966 And we'll talk about why linearity is not good 29 00:01:30,966 --> 00:01:36,300 and how we want more non-linearity in our network for image recognition. 30 00:01:36,733 --> 00:01:38,700 Then we'll talk about step two pooling. 31 00:01:38,700 --> 00:01:41,233 And we'll understand, how pooling works. 32 00:01:41,233 --> 00:01:43,100 We'll talk specifically about max pooling. 33 00:01:43,100 --> 00:01:47,433 And we'll also mention a couple of things about a mean pooling or some pooling and, 34 00:01:47,900 --> 00:01:51,100 other approaches that you can take to the process of pooling. 35 00:01:51,900 --> 00:01:55,433 Also in this lecture we'll have a really cool example. 36 00:01:55,433 --> 00:01:59,633 So there'll be a, very visual interactive tool that we're going to look at. 37 00:01:59,633 --> 00:02:02,733 So make sure to stick around to the end of that lecture, because that's 38 00:02:02,733 --> 00:02:06,333 going to add a lot of value to your learning process. 39 00:02:06,633 --> 00:02:08,700 What we're going to discuss at the end there. 40 00:02:08,700 --> 00:02:10,600 step three flattening. 41 00:02:10,600 --> 00:02:11,800 So here we will. 42 00:02:11,800 --> 00:02:13,000 It's going to be a quick tutorial 43 00:02:13,000 --> 00:02:16,966 on how to proceed from your pooled layers to your flattened layer. 44 00:02:17,133 --> 00:02:19,400 And then we're going to talk about a full connection. 45 00:02:19,400 --> 00:02:24,433 So this is the the very, meaty tutorial that puts everything together 46 00:02:24,433 --> 00:02:25,933 and puts everything into perspective 47 00:02:25,933 --> 00:02:30,400 and actually shows you how everything works at the end of the day 48 00:02:30,400 --> 00:02:34,200 and how those final neurons understand how to classify your image. 49 00:02:34,200 --> 00:02:36,066 Very, very important tutorial. 50 00:02:36,066 --> 00:02:38,833 and hopefully that will, 51 00:02:38,833 --> 00:02:42,033 summarize or kind of, put everything together for you. 52 00:02:42,366 --> 00:02:45,066 And finally we'll have a summary which will summarize everything 53 00:02:45,066 --> 00:02:46,400 we've talked about. 54 00:02:46,400 --> 00:02:49,033 And as an extra little feature, I've included 55 00:02:49,033 --> 00:02:52,033 a tutorial on softmax and cross entropy. 56 00:02:52,100 --> 00:02:55,433 So you don't have to take this tutorial, but I thought it'd be a great addition, 57 00:02:55,733 --> 00:02:59,300 of knowledge, because these are terms that you will come across 58 00:02:59,300 --> 00:03:01,900 when dealing with convolutional neural networks. 59 00:03:01,900 --> 00:03:04,500 So, maybe, maybe take it right away. 60 00:03:04,500 --> 00:03:08,066 Maybe, when you come across these terms, you can you will always know 61 00:03:08,066 --> 00:03:11,066 you can come back to this course and take, 62 00:03:11,066 --> 00:03:14,266 this tutorial to understand better what softmax and cross-entropy are. 63 00:03:14,600 --> 00:03:18,866 And also, as always throughout, these, tutorials, there will be 64 00:03:18,866 --> 00:03:22,666 lots of recommended reading for you to further upskill and get more knowledge. 65 00:03:23,233 --> 00:03:25,933 And on that note, I can't wait to see you on the first tutorial! 66 00:03:25,933 --> 00:03:27,233 This is going to be a very fun 67 00:03:27,233 --> 00:03:31,200 and exciting section, and until next time, enjoy deep learning!