Popular GitHub Repositories for Diminishing Returns

The unofficial python package that returns response of Google Bard through cookie value.

Returns latest research results by crawling arxiv papers and summarizing abstracts. Helps you stay afloat with so many new papers everyday.

The unofficial python package that returns response of Google Gemini through cookie values.

Takes in a users question and returns an answer along with URL resources.

Recurrent Neural Network for predicting Stock Returns

This is a text generation method which returns a generator, streaming out each token in real-time during inference, based on Huggingface/Transformers.

The PHP package that returns response of Google Bard through API.

The unofficial Python package that returns the response from Google Gemini using a cookie value, using reverse engineering. Demo: https://t.me/bard_kpbot/

Use langchain to create a model that returns answers based on online PDFs that have been read.

Writing assistant that edits .docx files and returns output with track changes.

NeurIPS 2019: DQN(λ) = Deep Q-Network + λ-returns.

GenAI and Image-based food classifier that returns nutritional estimates, recipe suggestions, and nearby restaurants — built with Azure AI Services.

AiCC Comments Reply with GPT(OpenAi) is a plugin that uses OpenAI's GPT to automatically reply to comments on your WordPress website. The plugin adds a "Reply with AI" button to the comment line actions, which when clicked, sends the comment text to OpenAI's GPT API and returns a suggested response ...

AiCC Comments Reply with GPT(OpenAi) is a plugin that uses OpenAI's GPT to automatically reply to comments on your WordPress website. The plugin adds a "Reply with AI" button to the comment line actions, which when clicked, sends the comment text to OpenAI's GPT API and returns a suggested response ...

This repo aims to train neural networks with pytorch. The data are normalized insede the class. The validation is performed automatically. The output model gets the unscaled data and returns the output unscaled (The scaling is performed inside). This way the user do not interact the scaling,; however the user can select to not use the default scaling and scale the data before the training

Algorithmic Trading Model Development for BTC/USDT Crypto Market. Crafting models that outperform benchmarks, balancing returns and risk management in the dynamic BTC/USDT market.

A simple interface where users can search for contextually relevant text passages in documents. It employs DistilBERT model for semantic embeddings and FAISS for efficient similarity search. When users enter a query, the system returns the most contextually relevant text passage from the corpus and highlights the matched keywords.

In this project I have performed analysis and prediction on 1,3,and 5 year returns on 1064 mutual funds in India. I have scraped data from a website which is the most visited website for mutual fund investments.I have tested regression models linear model,SGD Regressor , Random Forest Regressor,Decision Tree Regressor,Ridge,MLP Regressor and linear model (Lasso).After which I have selected the best perorming model and performed Hyper parameter tuning and then deployed an interactive application which can generate the visualization and send an email with the visualization to the users email address.

A semantic search indexing system designed to efficiently retrieve top matching results from a database of 20 million documents. Given the embedding of a search query, it quickly identifies and returns the most relevant documents

Randomly partitions time series segments into train, development, and test sets; Trains multiple models optimizing parameters for development set, final cross-validation in test set; Calculates model’s annualized return, improvement from buy/hold, percent profitable trades, profit factor, max drawdown