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Convolutional Neural Networks || Part-02 || code

Convolutional Neural Networks || Part-02 || Code In this session, I delve into Convolutional Neural Networks (CNNs) and their applications for both tabular and image datasets. Specifically, I explore how to use CNNs with the CIFAR-10 and Fashion MNIST datasets from Keras. Key Concepts Covered: - Checkpoints: These allow you to save model weights during training, enabling you to resume training from where you left off. - Optimizer: The optimizer determines how the model's weights are updated during training. Common optimizers include Adam, SGD, and RMSprop. - Epochs: An epoch represents one complete pass through the entire training dataset. -Batch Size: The number of samples used in each iteration during training. Components of a CNN: 1. Convolution Layer: Applies multiple filters to the input, creating feature maps that capture relevant patterns. 2. Pooling Layer: Reduces the spatial dimensions of the feature maps, aiding in translation invariance. 3. ReLU (Rectified Linear Unit): Introduces non-linearity, allowing the network to learn complex patterns. Remember that all images used in this session were collected from the internet. Part-01 : [Watch here](   • Convolutional Neural Networks || Part-01  ) code: https://github.com/sajjadrahman56/Lea... Connect with me: - GitHub: [github. com/sajjadrahman56](https://github.com/sajjadrahman56) - Twitter: [@sajjadrahman56](  / sajjadrahman56  ) #ComputerVision #DigitalImaging #PixelData #ObjectDetection #Classification #OCR #OpticalCharacterRecognition #DeepLearning #ConvolutionalNeuralNetworks #CNN #ConvolutionLayer #PoolingLayer #ReLU #ComplexPatterns #AI #MachineLearning #CProgramming #ConsoleProject #MedicalSystem #Programming #CSE #CSEEngineering #MachineLearning #artificialintelligence #datascience #aicommunity #mlworkshop #TechEducation #innovation #technology #AIandML #AI #ann #deeplearning #neuron #neuralnetworks #sajjadrahman56 #CNN