Traindoo

A look at using the open-source Cleanlab package to automatically boost the accuracy of LLMs with a few lines of code.

How to Make Any LLM More Accurate with Just a Few Lines of Code

Feature Selection in python is the process where you automatically or manually select the features in the dataset that contribute most to your prediction.

Automatic Feature Selection in Python: An Essential Guide

Unconventional sentiment analysis with CatBoost. The result is comparable to BERT SOTA.

Unconventional Sentiment Analysis: BERT vs. Catboost

In case you missed it, I built a neural network to predict loan risk using a public dataset from LendingClub. Then I built a public API to serve the model’s predictions. That’s nice and all, but… how good is my model?

Can I Grade Loans Better Than LendingClub?

Sentiment Classification for 2019 Lok Sabha Elections Using Text Based Classifiers 

Yes, as the title says, it has been very usual talk among data-scientists (even you!) where a few say, TensorFlow is better and some say Keras is way good! Let’s see how this thing actually works out in practice in the case of image classification.

Tensorflow Vs. Keras: Comparison by building a model for image classification

We utilized LibriTTS dataset [90] to train the hierarchical speech synthesizer.

How We Used the LibriTTS Dataset to Train the Hierarchical Speech Synthesizer

Accurate labeling was shown to be crucial, as it improved the performance of a Convolutional Neural Network (CNN).

Weather Image Recognition with AI: Dataset Labeling, Model Training, and Real-world Usecases

Traindoois in the Fitness-Wellness Tech Industry

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