sonti6

awesome-chatgpt-prompts

This repo includes ChatGPT prompt curation to use ChatGPT better.

chroma

the AI-native open-source embedding database

cloud

The TensorFlow Cloud repository provides APIs that will allow to easily go from debugging and training your Keras and TensorFlow code in a local environment to distributed training in the cloud.

cloud-foundation-fabric

End-to-end modular samples for Terraform on GCP.

COVID-INDIA-EDA

COVID

Dada-Tweet-Analysis-

Twitter data analysis

datasciencecoursera

coursera data science

sonti6/awesome-chatgpt-prompts

This repo includes ChatGPT prompt curation to use ChatGPT better.

sonti6/chroma

the AI-native open-source embedding database

sonti6/cloud

The TensorFlow Cloud repository provides APIs that will allow to easily go from debugging and training your Keras and TensorFlow code in a local environment to distributed training in the cloud.

sonti6/cloud-foundation-fabric

End-to-end modular samples for Terraform on GCP.

sonti6/COVID-INDIA-EDA

COVID

sonti6/Dada-Tweet-Analysis-

Twitter data analysis

sonti6/datasciencecoursera

coursera data science

sonti6/first_streamlist.app

sonti6/Hello-World

Test repository

sonti6/IBM_Practice

sonti6/MachineLearning

My ML Projects

sonti6/SontiProjects

Various projects picked up from Kaggle and other websites data. Conducting EDA and presenting the ML model.

sonti6/LangChain-101-For-Beginners-Python

A repository of code samples and course information for the LangChain Python Course

sonti6/lit-gpt

Hackable implementation of state-of-the-art open-source LLMs based on nanoGPT. Supports flash attention, 4-bit and 8-bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.

sonti6/onnx

Open standard for machine learning interoperability

sonti6/OpenPipe

Turn expensive prompts into cheap fine-tuned models

sonti6/pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

sonti6/Telecom-Users-Data

Any business wants to maximize the number of customers. To achieve this goal, it is important not only to try to attract new ones, but also to retain existing ones. Retaining a client will cost the company less than attracting a new one. In addition, a new client may be weakly interested in business services and it will be difficult to work with him, while old clients already have the necessary data on interaction with the service. Accordingly, predicting the churn, we can react in time and try to keep the client who wants to leave. Based on the data about the services that the client uses, we can make him a special offer, trying to change his decision to leave the operator. This will make the task of retention easier to implement than the task of attracting new users, about which we do not know anything yet. You are provided with a dataset from a telecommunications company. The data contains information about almost six thousand users, their demographic characteristics, the services they use, the duration of using the operator's services, the method of payment, and the amount of payment. The task is to analyze the data and predict the churn of users (to identify people who will and will not renew their contract). The work should include the following mandatory items: Description of the data (with the calculation of basic statistics); Research of dependencies and formulation of hypotheses; Building models for predicting the outflow (with justification for the choice of a particular model) based on tested hypotheses and identified relationships; Comparison of the quality of the obtained models.

sonti6/tensorflow-onnx

Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX

sonti6/Word-Cloud-Experiments

Collecting various scripts and playing with the word cloud in python