Lets understand Python “random” Function and difference with “numpy random” funtion Python has a built-in module called random that provides various functions to generate random numbers and perform other random operations. Lets understand it with real examples, that will make it easier…
You can use Pandas for random sampling of Datasets The sample method in Pandas is a convenient way to randomly select rows or samples from a DataFrame or Series object. By default, the method returns a random sample of the…
Rawpixel.com Graph Convolutional Networks (GCNs) are a type of Spectral GNNs that extend the concept of convolutional neural networks (CNNs) to graph-structured data. The basic building block of a GCN is the graph convolutional layer, which is designed to aggregate…
The steps for NLP (Natural Language Processing) typically include the following: Simple Example of NLP pipeline using python This example uses the NLTK library in python to perform tokenization, stop word removal, and stemming on a sample text. The output…
You can read the Article in detail here. QuickStart First, you want to install the environment (assuming that you have conda installed) Please use the pytorch version that is specified in the requirements. Otherwise, this may cause some problems when…
To use RETVec with TF Lite, it’s essential to have tensorflow_text version 2.13 or higher and tensorflow version 2.13 or higher. Instructions for upgrading TensorFlow can be found at the provided link. This notebook demonstrates the process of creating, saving,…
One of my students ask me about this question, so I thought why not write a blog about it as it can help many beginners? One way to use Google Colaboratory for deep learning projects is to clone a GitHub…
The Naïve Bayes algorithm is one of the fundamental things to study when studying statistics of artificial intelligence. Here I’m going to explain naive Bayes. It is very basic algorithm, yet one of most usefulusefull untill now. Probability vs likelihood.…
Dive into the World of EXL2 Quantization and Its Impact on AI Efficiency Introduction Reducing the size and speeding up the processing of Large Language Models (LLMs) is commonly achieved through quantization, with GPTQ emerging as a standout method for…
What is Imbalance Data? Imbalanced data refers to a classification problem where the number of observations belonging to one class significantly outnumbers the number of observations belonging to the other classes. This can lead to a classifier that is biased…