arpine-aikoda Goto Github PK

ai-autoencoder-for-http-log-anomaly-detection

AI: Autoencoder for HTTP Log Anomaly Detection

amazon-comprehend-examples

amazon-textract-and-comprehend-medical-document-processing

anomaly-detection-in-logs

Using Unsupervised methods to identify anomalies in user behaviour through IP Profiling

avatargan

Generate Cartoon Images using Generative Adversarial Network

awesome-colab-notebooks

Collection of google colaboratory notebooks for fast and easy experiments

awesome-google-colab

Google Colaboratory Notebooks and Repositories (by @firmai)

awesome-log-analysis

A list of awesome research on log analysis, anomaly detection, fault localization, and AIOps

awesome-medical-coding-nlp

A collection of papers in automated medical coding from free-texts :)

background-removal

Background removal of an image using OpenCV and Deep Learning.

barbershop

Barbershop: GAN-based Image Compositing using Segmentation Masks (SIGGRAPH Asia 2021)

celebamask-hq

A large-scale face dataset for face parsing, recognition, generation and editing.

deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

deep-loglizer

deep-patient-representation-mimiciii-multitask

Implementation of Deep Patient Representation of Clinical Notes at Intensive Care Unit for Multi-Task Prediction

deeplog

Anomaly detection is a critical step towards building a secure and trustworthy system. The primary purpose of a system log is to record system states and significant events at various critical points to help debug system failures and perform root cause analysis. Such log data is universally available in nearly all computer systems. Log data is an important and valuable resource for understanding system status and performance issues; therefore, the various system logs are naturally excellent source of information for online monitoring and anomaly detection. We propose DeepLog, a deep neural network model utilizing Long Short-Term Memory (LSTM), to model a system log as a natural language sequence. This allows DeepLog to automatically learn log patterns from normal execution, and detect anomalies when log patterns deviate from the model trained from log data under normal execution.

deeplog-1

Pytorch Implementation of DeepLog.

deeplog-2

PyTorch implements "DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning"

deeplog-3

deeplog-4

PyTorch implementation of Deeplog: Anomaly detection and diagnosis from system logs through deep learning

deeplog-5

This is the realization of core DeepLog

deeplog-introduction

DeepLog-Anomaly Detection and Diagnosis from System Logs through Deep Learning

emoji-creation-with-deep-learning

Trained a Keras model for recognizing facial expressions using the FER2013 dataset and predicted the emotions. Then, mapped emojis with different facial expressions and displayed the relevant emoji after prediction in a GUI using OpenCV.

emoji-prediction-using-deep-learning

This project aims to understand the underlying semantics of the text sentence using natural processing techniques to predict reasonable emojis based on the context.

emr-vis-web

An Interactive Tool for Natural Language Processing on Clinical Text

explainable-automated-medical-coding

Implementation and demo of explainable coding of clinical notes with Hierarchical Label-wise Attention Networks (HLAN)

face-parsing.pytorch

Using modified BiSeNet for face parsing in PyTorch

flaskseries

This is the Code from my Flask Series - JimShapedCoding. Each Folder is a checkpoint where we stopped at that particular episode number

hair-segmentation

hair segmentation in mobile device

homemade-machine-learning

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained