An AI made using artificial intelligence (AI) and machine learning algorithms (ML) .

Overview

DTech.AIML

An AI made using artificial intelligence (AI) and machine learning algorithms (ML) . This is created by help of some members in my team and they have a vast big contribution in this project.

Any way welcome to the DTech which stands for Darshan Technologies here we are creating projects using AI artificial intelligence and ML that is machine learning algorithms. Recently we have made a stock market value predicting model and this very profitable in all the case .

Any way hope that if you are reading this documentation to the end then we hope that you will be Stay forever with us helping in builting new project and to get new project ideas.

Team Members :::--->>> ______________________: 1)Darshan 2)Shubham 3)Arijit

Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models

Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models Abstract Many applications of generative models rely on the marginali

Stanford Intelligent Systems Laboratory 9 Jun 06, 2022
Implementation for the "Surface Reconstruction from 3D Line Segments" paper.

Surface Reconstruction from 3D Line Segments Surface reconstruction from 3d line segments. Langlois, P. A., Boulch, A., & Marlet, R. In 2019 Internati

85 Jan 04, 2023
Self-Adaptable Point Processes with Nonparametric Time Decays

NPPDecay This is our implementation for the paper Self-Adaptable Point Processes with Nonparametric Time Decays, by Zhimeng Pan, Zheng Wang, Jeff M. P

zpan 2 Sep 24, 2022
This is an early in-development version of training CLIP models with hivemind.

A transformer that does not hog your GPU memory This is an early in-development codebase: if you want a stable and documented hivemind codebase, look

<a href=[email protected]"> 4 Nov 06, 2022
Official Python implementation of the 'Sparse deconvolution'-v0.3.0

Sparse deconvolution Python v0.3.0 Official Python implementation of the 'Sparse deconvolution', and the CPU (NumPy) and GPU (CuPy) calculation backen

Weisong Zhao 23 Dec 28, 2022
Coursera - Quiz & Assignment of Coursera

Coursera Assignments This repository is aimed to help Coursera learners who have difficulties in their learning process. The quiz and programming home

浅梦 828 Jan 04, 2023
Seq2seq - Sequence to Sequence Learning with Keras

Seq2seq Sequence to Sequence Learning with Keras Hi! You have just found Seq2Seq. Seq2Seq is a sequence to sequence learning add-on for the python dee

Fariz Rahman 3.1k Dec 18, 2022
Traductor de lengua de señas al español basado en Python con Opencv y MedaiPipe

Traductor de señas Traductor de lengua de señas al español basado en Python con Opencv y MedaiPipe Requerimientos 🔧 Python 3.8 o inferior para evitar

Jahaziel Hernandez Hoyos 3 Nov 12, 2022
Training RNNs as Fast as CNNs

News SRU++, a new SRU variant, is released. [tech report] [blog] The experimental code and SRU++ implementation are available on the dev branch which

ASAPP Research 2.1k Jan 01, 2023
Unofficial Tensorflow-Keras implementation of Fastformer based on paper [Fastformer: Additive Attention Can Be All You Need](https://arxiv.org/abs/2108.09084).

Fastformer-Keras Unofficial Tensorflow-Keras implementation of Fastformer based on paper Fastformer: Additive Attention Can Be All You Need. Tensorflo

Yam Peleg 10 Jan 30, 2022
Chatbot in 200 lines of code using TensorLayer

Seq2Seq Chatbot This is a 200 lines implementation of Twitter/Cornell-Movie Chatbot, please read the following references before you read the code: Pr

TensorLayer Community 820 Dec 17, 2022
AFLFast (extends AFL with Power Schedules)

AFLFast Power schedules implemented by Marcel Böhme [email protected]

Marcel Böhme 380 Jan 03, 2023
AAAI 2022 paper - Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction

AT-BMC Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction (AAAI 2022) Paper Prerequisites Install pac

16 Nov 26, 2022
Unofficial implementation of PatchCore anomaly detection

PatchCore anomaly detection Unofficial implementation of PatchCore(new SOTA) anomaly detection model Original Paper : Towards Total Recall in Industri

Changwoo Ha 268 Dec 22, 2022
Data Augmentation Using Keras and Python

Data-Augmentation-Using-Keras-and-Python Data augmentation is the process of increasing the number of training dataset. Keras library offers a simple

Happy N. Monday 3 Feb 15, 2022
Count GitHub Stars ⭐

Count GitHub Stars per Day ⭐ Track GitHub stars per day over a date range to measure the open-source popularity of different repositories. Requirement

Ultralytics 20 Nov 20, 2022
Repository for the "Gotta Go Fast When Generating Data with Score-Based Models" paper

Gotta Go Fast When Generating Data with Score-Based Models This repo contains the official implementation for the paper Gotta Go Fast When Generating

Alexia Jolicoeur-Martineau 89 Nov 09, 2022
Kernel Point Convolutions

Created by Hugues THOMAS Introduction Update 27/04/2020: New PyTorch implementation available. With SemanticKitti, and Windows supported. This reposit

Hugues THOMAS 584 Jan 07, 2023
Code for paper ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop.

Who Left the Dogs Out? Evaluation and demo code for our ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization

Benjamin Biggs 29 Dec 28, 2022
Algorithm to texture 3D reconstructions from multi-view stereo images

MVS-Texturing Welcome to our project that textures 3D reconstructions from images. This project focuses on 3D reconstructions generated using structur

Nils Moehrle 766 Jan 04, 2023