628 Open source projects that are alternatives of or similar to Deep Plant

Models and code related to sketch simplification of rough sketches.

Ordered Neurons LSTM

MIVisionX toolkit is a comprehensive computer vision and machine intelligence libraries, utilities and applications bundled into a single toolkit.

Moore Machine Networks (MMN): Learning Finite-State Representations of Recurrent Policy Networks

Connectionist Temporal Classification (CTC) decoding algorithms: best path, prefix search, beam search and token passing. Implemented in Python.

Sequence Labeling in Tensorflow

Rare pupper image compression model for word-embedding-esque operations.

Human activity recognition using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six categories (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING).

🚀 😏 Near Real Time CPU Face detection using deep learning

A robot learning from demonstration framework that trains a recurrent neural network for autonomous task execution

Semantic segmentation on iPhone using ESPNetv2

PyTorch code for CVIU paper "AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction"

Real-time people Multitracker using YOLO v2 and deep_sort with tensorflow

Self driving car in GTAV with Deep Learning

This repository contains the code for a conference paper "Predicting the football match winner using LSTM model of Recurrent Neural Networks" that we wrote

Digit recognition with Convolutional Neural Networks in WebGL

CNN visualization tool in TensorFlow

RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above versions. RNNSharp supports many different types of networks, such as forward and bi-directional network, sequence-to-sequence network, and different types of layers, such as LSTM, Softmax, sampled Softmax and others.

Codes and Templates from the SuperDataScience Course

A bidirectional recurrent neural network model with attention mechanism for restoring missing punctuation in unsegmented text

Probabilistic Dialogue Act Classification for the Switchboard Corpus using an LSTM model

A Recurrent Neural Network to predict Bitcoin value

NLP tutorial

ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

TensorFlow implementation of "Tracking the World State with Recurrent Entity Networks".

Learning Fine-grained Image Similarity with Deep Ranking is a novel application of neural networks, where the authors use a new multi scale architecture combined with a triplet loss to create a neural network that is able to perform image search. This repository is a simplified implementation of the same

[CVPR 2020] MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning

TensorFlow implementation of multi-layer recurrent neural networks for training and sampling from texts

SincNet is a neural architecture for efficiently processing raw audio samples.

Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics.

TensorFlow Implementation of Twitter Chatbot

Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.

Comparing Fixed and Adaptive Computation Time for Recurrent Neural Networks

Keras implementation of the multi-channel cascaded architecture introduced in the paper "Brain Tumor Segmentation with Deep Neural Networks"

Automate swing trading using deep reinforcement learning. The deep deterministic policy gradient-based neural network model trains to choose an action to sell, buy, or hold the stocks to maximize the gain in asset value. The paper also acknowledges the need for a system that predicts the trend in stock value to work along with the reinforcement …

Corruption and Perturbation Robustness (ICLR 2019)

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

Binary Convolution Network for faster real-time processing in ASICs

Implementation based on the paper Li et al., “A Convolutional Neural Network Cascade for Face Detection, ” 2015 CVPR

Efficient Learning of Augmentation Policy Schedules

This is just a collection of projects that made during my DEEPLEARNING NANODEGREE by UDACITY

A multi-channel neural network audio classifier using Keras

PyTorch reimplementation of Interactive Deep Colorization

🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.

A Python package for gender classification.

[CVPR2020] GhostNet: More Features from Cheap Operations

Udacity Self-Driving Car Engineer Nanodegree. Project: Build a Traffic Sign Recognition Classifier

Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition (https://arxiv.org/pdf/2006.11538.pdf)

Sequence prediction using recurrent neural networks(LSTM) with TensorFlow

Master Thesis on Bayesian Convolutional Neural Network using Variational Inference

Keras (tensorflow) implementation of SincNet (Mirco Ravanelli, Yoshua Bengio - https://github.com/mravanelli/SincNet)

Tutorials on how to implement a few key architectures for image classification using PyTorch and TorchVision.

A image registration method using convolutional neural network features.

🎨 A CNN visualizer

Play deep learning with CIFAR datasets

Tensorflow implementation of our paper: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

Kaggle | 9th place single model solution for TGS Salt Identification Challenge

Velocity in deep-learning research