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abigsurvey

A collection of 700+ survey papers on Natural Language Processing (NLP) and Machine Learning (ML)

awesome-deepfakes-materials

A curated list of awesome Deepfakes materials

awesome-face-forgery-generation-and-detection

A curated list of articles and codes related to face forgery generation and detection.

awesome-interaction-aware-trajectory-prediction

A selection of state-of-the-art research materials on trajectory prediction

awesome-transformers-in-medical-imaging

A collection of resources on applications of Transformers in Medical Imaging.

biogpt

conv-social-pooling

Code for model proposed in: Nachiket Deo and Mohan M. Trivedi,"Convolutional Social Pooling for Vehicle Trajectory Prediction." CVPRW, 2018

coursera

git tutorial

datasets

Machine learning datasets used in tutorials on MachineLearningMastery.com

deepfake-detection

The Pytorch implemention of Deepfake Detection based on Faceforensics++

deeplake

Data Lake for Deep Learning. Multi-modal Vector Database for LLMs/LangChain. Store, query, version, & visualize datasets. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai

deeplearning-papernotes

Summaries and notes on Deep Learning research papers

detect-gpt

DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature

dfdc_deepfake_challenge

A prize winning solution for DFDC challenge

dlwpt-code

Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.

encoder_decoder

Four styles of encoder decoder model by Python, Theano, Keras and Seq2Seq

fastai

The fastai deep learning library, plus lessons and and tutorials

fastbook

The fastai book, published as Jupyter Notebooks

go

The Open Source Data Science Masters

hands-on-recommendation-systems-with-python

Hands-On Recommendation Systems with Python published by Packt

har-keras-coreml

Human Activity Recognition (HAR) with Keras and CoreML

har-lstm-keras

Human activity detection using LSTM in keras

hello-world

GitHub Tutorial

human-activity-recognition

Use a LSTM network to predict human activities from sensor signals collected from a smartphone

human-activity-recognition-with-neural-network-using-gyroscopic-and-accelerometer-variables

The VALIDATION ACCURACY is BEST on KAGGLE. Artificial Neural Network with a validation accuracy of 97.98 % and a precision of 95% was achieved from the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this: The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.

infer

The code & datasets for the paper INFER: INtermediate representations for FuturE pRediction

inltk

Natural Language Toolkit for Indic Languages aims to provide out of the box support for various NLP tasks that an application developer might need

introtodeeplearning_labs

Lab Materials for MIT 6.S191: Introduction to Deep Learning

keras

Deep Learning for humans

keras-seq-2-seq-signal-prediction

An implementation of a sequence to sequence neural network using an encoder-decoder