Described in this article here, is a novel deep convolutional generative neural network called the Deep-Satellite. It was developed to predict the changes in cropland satellite imagery data for Sri Lanka. The goal of the Deep-Satellite model was to forecast the one time-step ahead satellite radar data.
Tracking the versions of various machine learning libraries used in a particular machine learning project is important. For example, this version tracking helps understand and quantify the associated model drift.
Described in this post is a simple Python script to track the versions of commonly used machine learning libraries.
This code notebook describes the use of tensor slicing to build higher dimensional neural networks.
In a typical deep neural network architecture designed to handle image data, the input data is represented as three dimensional (3D) tensors.
The information represented in these tensors are the pixel values and the color channel information.
Described in this article is a simple deep convolutional network (CNN), with a state-of-the-art EfficientNet backbone and an attention attention mechanism, to classify the severity of diabetic retinopathy in retinal images. The model can be trained using the publicly available diabetic retinopathy dataset on Kaggle.
In this post, the attention mechanism for neural networks is explained. The explanation has two parts. The first part deals with the intuitive understanding of the attention mechanism in a neural network. The second part implements an attention function using Tensorflow-Keras in Python3.
Described in this blog post is a prototype deep neural network model for predicting the genome sequence of the most prevalent SARS-CoV2 mutant variant.
The various mutant SARS-CoV2 variants' genomic data were sourced from the Nucleotide database of the National Library of Medicine (NLM).
The deep neural network prototyping was done using Python and Keras recurrent neural network API.
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