Convolutional Neural Network (CNN): Introduction and Implementation

#cnn #neuralnetworks #deeplearning #machinelearning #ai In this video, I introduced convolutional neural network (CNN) and showed it’s implementation. CNN is a type of deep learning algorithm mainly used for processing and analyzing image data by automatically learning spatial features through backpropagation. It consists of layers such as convolutional layers, pooling layers, and fully connected layers, enabling it to efficiently recognize patterns and objects in images and videos. Github repository for the code: https://github.com/manishasirsat/cnn-implementation -------------------------------------------------------------------------------------------------------------------------------------------------------------- Generative AI Playlist: https://www.youtube.com/watch?v=ID04YmgzM38&list=PLzkBTicHqQFmdF62zHHramnBRZl6zUvmR Deep Learning Playlist: https://www.youtube.com/playlist?list=PLzkBTicHqQFmY89SS1Xkfe-gpGCr-XGJo&jct=a2fQJbo-0p7KqCRdDNC9lSdLYCPcag -------------------------------------------------------------------------------------------------------------------------------------------------------------- Connect with me on social media platforms: Website: https://ai-researchstudies.com/ Google scholar: https://scholar.google.com/citations?user=kM4QN-8AAAAJ&hl=en LinkedIn: https://www.linkedin.com/in/manishasirsat GitHub:https://github.com/manishasirsat Quora: https://machinelearningresearch.quora.com/ Blogger: https://manisha-sirsat.blogspot.com/ Twitter: https://twitter.com/ManishaSirsat ⏱️ Timestamps 0:00 Intro 0.27 CNN intro 4:21 input layer 4:50 convolutional layer 5:55 Filter/kernel 9:35 Stride 13:18 Padding 14:32 activation function 17:32 pooling layer 19:51 flattening layer 20:40 fully connected layer 21:41 output layer 23:58 backpropogation 25:25 Loss function 26:25 optimizer 27:05 CNN hyper-parameters 28:59 CNN implementation