Diving Deep into Pooling: The Backbone of CNN EfficiencyThe concept of pooling layers has been integral to Convolutional Neural Networks (CNNs) since their early development. The seminal work on…Nov 26, 2024Nov 26, 2024
Understanding Batch Normalization in Deep LearningIn deep learning, ensuring the stability and efficiency of neural networks involves numerous techniques, one of which is Batch…Nov 21, 2024Nov 21, 2024
Gradient Descent (GD)Gradient Descent (GD) is fundamental in training neural networks, enabling them to learn from data by updating the weights of the network…Nov 19, 2024Nov 19, 2024
How Artificial Neural Networks Mimic Human CognitionArtificial Neural Networks (ANNs) stand at the forefront of AI, driving advancements that emulate the human brain’s intricate networks…Nov 19, 2024Nov 19, 2024
Exploring Activation Functions: The Building Blocks of Neural NetworksActivation functions in neural networks serve as mapping mechanisms that transform the weighted sum of a neuron’s inputs into another…Nov 19, 2024Nov 19, 2024
Extracting patches from Whole Slide Images(WSI)Digital pathology has emerged with the digitization of patient tissue samples and in particular the use of digital whole slide images…Jul 9, 2021Jul 9, 2021
