marktechpost

Building an Advanced Convolutional Neural Network with Attention for DNA Sequence Classification and Interpretability

In this tutorial, we take a hands-on approach to building an advanced convolutional neural network for DNA sequence classification. We focus on simulating real biological tasks, such as promoter ...
GitHub

BatchNormalization fails after Concatenation of masked Embeddings

import keras import numpy as np rng = np.random.default_rng(123) data_1 = rng.choice(9, size=(50, 20)) + 1 data_2 = rng.choice(2, size=(50, 20)) + 1 y = rng.normal(0 ...
Hosted on MSN

Layer Normalization In Transformers — Simplest Explanation Ever!

Still confused about layer normalization? This beginner-friendly breakdown makes it crystal clear. #LayerNormalization #AIForBeginners #TransformersExplained #LayerNormalization #Transformers ...
IEEE

An Efficient Layer Normalization Training Module With Dynamic Quantization for Transformers

Abstract: Layer normalization (LN) function is widely adopted in Transformer-based neural networks. The efficient training of Transformers on personal devices is attracting attention for data privacy ...
marktechpost

Dynamic Tanh DyT: A Simplified Alternative to Normalization in Transformers

Normalization layers have become fundamental components of modern neural networks, significantly improving optimization by stabilizing gradient flow, reducing sensitivity to weight initialization, and ...
IEEE

Experimenting With Normalization Layers in Federated Learning on Non-IID Scenarios

Abstract: Training Deep Learning (DL) models require large, high-quality datasets, often assembled with data from different institutions. Federated Learning (FL) has been emerging as a method for ...
GitHub

plant-disease-detection-using-keras.ipynb

"from keras.layers.normalization import BatchNormalization\n", "from keras.layers.convolutional import Conv2D\n", "from keras.layers.convolutional import MaxPooling2D ...