Convolutional Neural Networks || Part-01
Convolutional Neural Networks || Part-01
In this session I described how computers interpret digital images, extracting key information by reading images as arrays of pixels, with values ranging from 0-255.
And the different types of Computer Vision, including Object Detection, Classification, and Optical Character Recognition (OCR).
But that's not all! I'll also take a deep dive into the architecture of Convolutional Neural Networks (CNNs), a class of deep neural networks most commonly applied to analyzing visual imagery.
In each convolution of a CNN, there are three key parts:
1. Convolution Layer: This is where we apply multiple filters to the input and create a feature map.
2. Pooling Layer: Here, we reduce the spatial size (width and height) of the input volume. This serves to decrease the computational complexity for upcoming layers.
3. ReLU (Rectified Linear Unit): This introduces non-linearity into the network, allowing us to learn more complex patterns.
copyright : all images collect from internet
Connect with me
GitHub code: https://github.com/sajjadrahman56
🐤Twitter: / sajjadrahman56
#ComputerVision #DigitalImaging #PixelData #ObjectDetection #Classification #OCR #OpticalCharacterRecognition #DeepLearning #ConvolutionalNeuralNetworks #CNN #ConvolutionLayer #PoolingLayer #ReLU (Rectified Linear Unit)#ComplexPatterns#AI #MachineLearning
#cprogramming #cosoleproject #medicalsystem #programming #programing #cse #cseengineering