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 ...

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 ...

Speech and language processing. At the end of the beginning. The old cliche "a picture is worth a thousand words" might be even more true when working with complex machine learning models. Unless you ...

Abstract: Detecting curbs efficiently and at a low cost is essential for autonomous driving. Most of the existing methods rely on light detection and ranging, which is a complicated and costly ...

Abstract: Fire is the main disaster that causes economic losses and threats to life safety. The target detector can detect the flame and send an alarm in the early stage of the fire, preventing the ...

The code above raises the following exception: TypeError: Exception encountered when calling layer "batch_normalization_1" (type BatchNormalization). Input 'y' of 'AddV2' Op has type float32 that does ...

Convolutional neural networks (CNNs) are composed of various layers, including convolution, pooling, and dense layers. Batch normalisation is used to stabilise learning and improve network performance ...

In this article, we will explore Keras tokenizer through which we will convert the texts into sequences that can be further fed to the predictive model. Keras Tokenizer is essential for converting ...