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Kathan Soni

calculate the standard deviation or variance of the tensor in Tensorflow

Easy way to calculate the standard deviation or variance of the tensor in TensorFlow?

Let’s see how you can calculate the standard deviation or variance of the tensor. As you know while building a neural network, you have to perform aggregation on the tensor often. Finding maximum, minimum, and sum is done via the same piece of code with very little change in it. But finding variance or standard deviation will require some tweaks.… Continue readingEasy way to calculate the standard deviation or variance of the tensor in TensorFlow?

Chat GPT-3 web app with streamlit

How to Create A Chat GPT-3 Web app with Streamlit in Python

OpenAI’s ChatGPT-3(Generative Pre-trained Transformer) is one of the most powerful AI chatbots right now. You can ask anything and it gives the precise answers of it. OpenAI also provides API to implement it in the programming. In this article, we will see how to create a Chat GPT-3 web app with Streamlit in python using API. After creating this, your… Continue readingHow to Create A Chat GPT-3 Web app with Streamlit in Python

Recursive Feature Elimination in machine learning

Recursive Feature Elimination(RFE): Effective Feature selection method in machine learning

To reduce features in a dataset, two methods are there in machine learning: Feature selection and Feature extraction. Recursive feature elimination or in short RFE is the feature selection method. Here is given a complete guide on Recursive Feature Elimination for feature selection in machine learning. What is RFE? In Feature extraction, two or more features are combined from existing… Continue readingRecursive Feature Elimination(RFE): Effective Feature selection method in machine learning

Plotly vs Matplotlib: Which is best for data visualization in python?

There are many libraries in python available for making charts from the data. By which you can create charts and derive underlying information from the data. Plotly and Matplotlib are two of the libraries for it. Let’s see the comparison between Plotly vs Matplotlib and see which is best for performing the best data visualization in python. What is Matplotlib… Continue readingPlotly vs Matplotlib: Which is best for data visualization in python?

Naive Bayes Classifier in machine learning

Naive Bayes Classifier in machine learning- All you need to know

Classification machine learning algorithms use probability to predict the correct class, such as Logistic regression, SVC, decision tree classifier, Lasso regression, etc. Naive Bayes is the supervised machine learning algorithm used for classification problems. Let’s see how it works. Principle of Naive Bayes classifier The Naive Bayes classifier is one of the fastest and simplest algorithms in machine learning. The… Continue readingNaive Bayes Classifier in machine learning- All you need to know

pipeline in machine learning

How to make a useful pipeline in machine learning using sklearn

A machine learning pipeline consists of multiple data extraction, preprocessing, and model-building steps. It helps to automate processes that are required in model building. Pipeline helps to include all steps of preprocessing, feature selection, feature extraction, model selection, and model building In one entity. Here we will see how to make a pipeline in machine learning.

logging in python

What is logging and How to make it from scratch in python

With the help of python, we can build many projects of web scraping, data science, and more. When you are building a project, logging is also an essential part of that. There are many benefits of logging into the program. Here we will talk about what is logging in python and how to do it by the in-built library as well as by building it from scratch.

precision,recall and F1-score

When to use Precision, Recall, or F1-score?

There are mainly two types of problems with machine learning. The first one is regression and the second is a classification problem. After building any model, its evaluation is a very important part of making the best model and optimizing it. Here you will know when to use Precision, Recall, or F1 score to evaluate the classification problem.