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Research

Research

1 - Reinforcement Learning

Reinforcement Learning

Reinfocement Learning Course

Algorithm

  • reinforcement learning algorithms This repository contains most of pytorch implementation based classic deep reinforcement learning algorithms, including - DQN, DDQN, Dueling Network, DDPG, SAC, A2C, PPO, TRPO.

2 - PhD How To

PhD How To

3 - Digital Twin News

Digital Twin News

Digital Twin Policy

Digital Twin News

Digital Twin Tools

Digital Twin DIY

Digital Twin on Github

Digital Twin Research Group

4 - Generative Machine Learning GAN

Generative Machine Learning GAN

GAN

The dataset was web-scraped for an original 20k samples, then a custom MRCNN model was trained for image segmentation and cropping before being fed into the 128 DCGAN, trained on local hardware, 1660

Generative Model Course

GAN Course

GAN Project/Paper

Style GAN

Paper

GAN Image Superresolution

GAN

Style-GAN

Research

Research

  • Realless Generative webs with blinking eyes

Music Generation

Video Generation

  • Hyperchroma A music player that creates real-time music videos (hyperchroma.app)

Colorization

  • Pix2pix vs CycleGAN
    • Cycle-GAN can work in an ‘unpaired’ manner and various architectural differences. Unpaired image translation is much harder, as you demand the model to learn objects of different shapes, size, angle, texture, location in different scenes and settings on top of the actual task (coloring in this case). Requires more data and you don’t have fine control on the learning. Formulating the coloring problem as a paired task makes more sense as you simply decrease the complexity of the problem without increasing data collection/annotation work.
    • The whole point about using CycleGAN is that it can learn in unpaired situations. And it works well in the context of style transfer tasks where the changes are really bold and less nuanced. But, in the context of image colorization, the changes are really subtle and also there are way more options to choose colors than changing a horse to zebra. The other thing is that learning to change a colored image to black and white is much easier for the model than learning to colorize it which can lead to a bad learning procedure.
    • The most prominent differences is that CycleGAN helps when you have unpaired images and you want to go from one class to the other (Horse to Zebra for example) but in the Pix2Pix paper, the images that you get after the inference, are the input images but with some new features (black&white to colorized or day time to night time of a scene). In pix2pix, a conditional GAN (one generator and one discriminator) is used with some supervision from L1 loss. In CycleGAN, you need two generators and two discriminators to do the task: one pair for going from class A to B and one pair for going from class B to A. Also you need Cycle Consistency Loss to make sure that the models learn to undo the changes they make.
  • pix2pixhd is pix2pix in higher resolution
  • pix2pix tutorial and example:

Machine Learning: Image Generator

GAN

Generation

Lightboard

5 - Awesome Research and Academic Writing Assistant Tools

Awesome Research and Academic Writing Assistant Tools

Related links:
🔗 Paper and Article Search Tools
🔗 Awesome Writing Assistant Tools
🔗 Awesome Research and Academic Writing Assistant Tools
🔗 Awesome Science Blogs and Forums
🔗 Awesome Scientific Journal Tools
🔗 Awesome Conferences Tools
🔗 Awesome Latex Apps

Chrome Extension and Script

Multitools

Arxiv

PubMed and Google Scholar

Code

Jupyter Notebook

Citation Manager

Paper Finder

MS Word Add-ons

Citation Manager

  • Zotero
  • Mendeley

Grammar

  • Grammarly
  • Writely
  • LanguageTools
  • WriteBetter
  • Power Thesaurus

Research

  • Keenious
  • Data Finder

Reference Tools

Article/Paper Summarization

Article/Paper Graph and Relationship

Article/Paper Reading

Article/Paper Exploration

Article/Paper Reading Tools

Web Annotation

Research Open Data

List of Researcher and Academia Tools

Research/Academia Forums

Testing

Paper Graph Writing Assistant

6 - Awesome Science Blogs and Forums

Awesome Science Blogs and Forums

Related links:
🔗 Paper and Article Search Tools
🔗 Awesome Writing Assistant Tools
🔗 Awesome Research and Academic Writing Assistant Tools
🔗 Awesome Science Blogs and Forums
🔗 Awesome Scientific Journal Tools
🔗 Awesome Conferences Tools
🔗 Awesome Latex Apps

7 - Paper and Article Search Tools

Paper and Article Search Tools

Related link:
🔗 Paper and Article Search Tools
🔗 Awesome Writing Assistant Tools
🔗 Awesome Research and Academic Writing Assistant Tools
🔗 Awesome Science Blogs and Forums
🔗 Awesome Scientific Journal Tools
🔗 Awesome Conferences Tools
🔗 Awesome Latex Apps

Article Search Flowchart

Guide to Finding Articles/Books - Google Docs

Article/Paper Search Tools

Paper Search Engine (Sci-hub)

Article

8 - Awesome Conferences Tools

Awesome Conferences Tools

Related links:
🔗 Paper and Article Search Tools
🔗 Awesome Writing Assistant Tools
🔗 Awesome Research and Academic Writing Assistant Tools
🔗 Awesome Science Blogs and Forums
🔗 Awesome Scientific Journal Tools
🔗 Awesome Conferences Tools
🔗 Awesome Latex Apps

AI Journals

9 - Machine Learning Teaching

Machine Learning Teaching

Teaching Deep Learning

Machine Learning : Visual Coding

💡 : Kid’s machine learning tutorial

Machine Learning on Spreadsheet

Machine Learning Visualization

Machine Learning

  • The First Rule of Machine Learning: Start without Machine Learning
  • Machine Learning: The High Interest Credit Card of Technical Debt – Google Research So little of success in ML comes from the sexy algorithms and so much just comes from ensuring a bunch of boring details get properly saved in the right place. After months learning about machine learning for time series forecasting, several chapters in a book on deep learning techniques for time series analysis and forecasting, the author kindly pointed out that there are no papers published up to that point that prove deep learning (neural networks) can perform better than classical statistics. Career lesson: Ask a lot of questions early in a project’s life. If you’re working on something that uses machine learning, ask what system it’s replacing, and make sure that someone (or you) runs it manually before spending the time to automate.

Rules of Machine Learning: | ML Universal Guides | Google Developers

Machine Learning

10 - Machine Learning News and Blog

Machine Learning News and Blog

Machine Learning News Update

Articles about Machine Learning

11 - Hardware for Machine Learning

Hardware for Machine Learning

Hardware for Deep Learning

TPU - GPU

  • Turning TPU into GPU (mean: it compiles Pytorch to work on a TPU). PyTorchXLA converts the TPUv3-8 hardware into a GPU so you can use it with PyTorch as a normal GPU. TPUv3-8 which is part of free access from Google Colab can give a computation power that is equivalent to 8 V100 Tesla GPU and possibly 6 3090RTX GPU. info is here. TPUs are ~5x as expensive as GPUs ($1.46/hr for a Nvidia Tesla P100 GPU vs $8.00/hr for a Google TPU v3 vs $4.50/hr for the TPUv2 with “on-demand” access on GCP).
  • We recommend CPUs for their versatility and for their large memory capacity. GPUs are a great alternative to CPUs when you want to speed up a variety of data science workflows, and TPUs are best when you specifically want to train a machine learning model as fast as you possibly can. In Google Colab, CPU types vary according to variability (Intel Xeon, Intel Skylake, Intel Broadwell, or Intel Haswell CPUs). GPUs were NVIDIA P100 with Intel Xeon 2GHz (2 core) CPU and 13GB RAM. TPUs were TPUv3 (8 core) with Intel Xeon 2GHz (4 core) CPU and 16GB RAM).

Free TPU

AI/ML Cloud Computing

Machine Learning

AI Platform

12 - Awesome List of Dataset

Awesome List of Dataset

Dataset

Art Dataset

Dataset

Drug Dataset

Dataset Zoo

Dataset

Dataset

Dataset

Dataset

Dataset Tools

Cell Tower Dataset

Twitter Dataset

Dataset

  • EleutherAI EleutherAI is a grassroots AI research group aimed at democratizing and open sourcing AI research.
  • The Pile : The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together.

13 - Sign Language Recognition

Sign Language Recognition

List of Project

Video-based

with NLP

Sign Language Web

Sign Language Tutor

Sign Language Vocalization

Inverse ( … to Sign Language)

Image-based

Bahasa Isyarat Indonesia

  • Bahasa Isyarat Indonesia (BISINDO)
    • dianggap sebagai bahasa yang bisa mewakili budaya tuli Indonesia
  • Sistem Isyarat Bahasa Indonesia (SIBI)
    • adopsi dari American Sign Language (ASL)
    • dipakai di SLB
  • Klobility - BISINDO dan SIBI: Apa Bedanya?

14 - Time Series Forecasting

Time Series Forecasting

LSTM for Time Series Forecasting

  • Univariate LSTM Models : one observation time-series data, predict the next value in the sequence
  • Multivariate LSTM Models : two or more observation time-series data, predict the next value in the sequence
    • Multiple Input Series : two or more parallel input time series and an output time series that is dependent on the input time series
    • Multiple Parallel Series : multiple parallel time series and a value must be predicted for each
  • Univariate Multi-Step LSTM Models : one observation time-series data, predict the multi step value in the sequence prediction.
  • Multivariate Multi-Step LSTM Models : two or more observation time-series data, predict the multi step value in the sequence prediction.
    • Multiple Input Multi-Step Output.
    • Multiple Parallel Input and Multi-Step Output.

Machine Learning for Multivariate Input

Statistical Method for Multivariate Input

Machine Learning for Univariate Input

Statistical Method for Univariate Input

Jupyter Notebook Examples

Univariate ARIMA

import statsmodels

Univariate LSTM

import keras

Multivariate VAR

(Note: VAR should only for Stationary process - Wikipedia)

Multivariate LSTM

Prophet and Kats from Facebook

Note on Multivariate and Univariate

Software

Other Time Series

Precipitation Forecasting

Deep Learning for Forecasting

top open source deep learning for time series forecasting frameworks.

  1. Gluon This framework by Amazon remains one of the top DL based time series forecasting frameworks on GitHub. However, there are some down sides including lock-in to MXNet (a rather obscure architecture). The repository also doesn’t seem to be quick at adding new research.
  2. Flow Forecast This is an upcoming PyTorch based deep learning for time series forecasting framework. The repository features a lot of recent models out of research conferences along with an easy to use deployment API. The repository is one of the few repos to have new models, coverage tests, and interpretability metrics.
  3. sktime dl This is another time series forecasting repository. Unfortunately it looks like particularly recent activity has diminished on it.
  4. PyTorch-TS Another framework, written in PyTorch, this repository focuses more on probabilistic models. The repository isn’t that active (last commit was in November).

eBook Forecasting

Timeseries Forecasting

Timeseries Forecasting Book

Timeseries Forecasting Reading

Timeseries RNN

15 - Soft Sensor

Soft Sensor

What is Soft Sensors

Soft-sensors: predictive models for sensor characteristic are called soft sensors Soft-sensors: software+sensor

Soft-sensor Categories

  • model-driven
    • First Principle Models (FPM)
    • extended Kalman Filter
  • data-driven
    • Principle Component Analysis + regression model, Partial Least Squares
    • Artificial Neural Networks
    • Neuro-Fuzzy Systems
    • Support Vector Machines

Soft-sensor Application

  • on-line prediction
    • prediction of process variables which can be determined either at low sampling rates
    • prediction of process variables which can be determined through off-line analysis only
    • (statistical or soft computing supervised learning approaches)
  • process monitoring
    • detection of the state of the process, usually by human
    • observation and interpretation of the process state (based on univariate statistics) and experience of the operator
  • process fault detection
    • detection of the state of the process

FPM

  • First Principle Models describe the physical and chemical background of the process.
  • These models are developed primarily for the planning and design of the processing plants, and therefore usually focus on the description of the ideal steady-states of the processes
  • based on established laws of physics
  • does not make assumptions such as empirical model and fitting parameters
  • using experimental data

Data-driven data-driven models are based on the data measured within the processing plants, and thus describe the real process conditions, they are, compared to the model-driven Soft Sensors, more reality related and describe the true conditions of the process in a better way. Nevertheless

The most commonly applied multivariate analysis tools are principal component analysis (PCA) for fault detection and projection of latent structures (PLS) for the prediction of key quality parameters at end of batch.

First-principle models may be the answer, using experimental data instead of statistical methods to estimate model parameters. They are not as quick and easy to build, but they have many advantages. In terms of simulation, first-principle models provide extrapolation in addition to the interpolation provided by data-driven models. But they also can be used for monitoring, control and optimization.

Soft-Sensor Modelling

Others

Dataset

Reference

Ebook

16 - Historical Colorization

Historical Colorization

Old Historical Image/Video

Colorization Tools

17 - Interesting Machine Learning Papers

Interesting Machine Learning Papers

Neural Representation

MPC Deep Learning Control

Deep Implicit Layers

18 - Machine Learning Tools

Machine Learning Tools

Machine Learning Toolbox

Machine Learning Deployment

Machine Learning Versioning Control

Data Studio

Machine Learning Ops

Machine Learning Toolbox

Machine Learning

Machine Learning Tools

Machine Learning Steps

Machine Learning

Machine Learning

Machine Learning Labeling

19 - Awesome List of Machine Learning Visualization

Awesome List of Machine Learning Visualization

Related links:
🔗 Awesome List of Data Visualization
🔗 Awesome List of Machine Learning Visualization
🔗 Awesome List of Interactive and Explorable Webs
🔗 Interactive Books

Machine Learning Visualization

20 - Machine Learning in Sports

Machine Learning in Sports

Baseball

Soccer

21 - Machine Learning for Image Processing

Machine Learning for Image Processing

Image to Art

Image Reconstruction and Upscaler

Image Cloaking

  • Fawkes : Prevent AI for identifying photo

Computer Vision Data Manipulation

  • cvdata (MIT) : Tools for creating and manipulating computer vision datasets: resize images, rename files, annotation format conversion, image format conversion, Split dataset into training, validation, and test subsets and much more.

ML-based Image Processor

  • Hotpot AI ; Design Assistant, starting from image processing, device mockups, to social media posts

22 - Machine Learning for Health

Machine Learning for Health

AI in Healths in Indonesia

  • Niko
  • Healhtech
  • Madeena
  • bravehealth.tech, rafi amjadrasyid
  • gigi.id
  • farmasee.id
  • ctscope
  • alinamed
  • Lamesia.com

Deep Learning for Medical Image Segmentation

Healthcare Apps

23 - Machine Learning in Agriculture

Machine Learning in Agriculture

Disease Detection

Crop Simulator and Simulation

Plant Phenomics

24 - Machine Learning for Earth Science

Machine Learning for Earth Science

AI for Earth Science

Climate

  • Climate indices (BSD-3-Clause) : Python implementations of various climate index algorithms which provide a geographical and temporal picture of the severity of precipitation and temperature anomalies useful for climate monitoring and research.
  • MIT Climate CoLab : A collaborative online community centered around a series of annual contests that seek out promising ideas for fighting climate change.
  • Climate Action Challenge : global design competition calling on the creative community to submit bold, innovative solutions to combat the impacts of climate change.
  • Improving Weather Prediction with CNN: J. A. Weyn, D. R. Durran, and R. Caruana, “Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere,”Journal of Advances in Modeling Earth Systems, vol. 12, no. 9, Sep. 2020,ISSN: 1942-2466.doi:10.1029/2020ms002109.[Online]. Available:http://dx.doi.org/10.1029/2020MS002109. Code: https://github.com/jweyn/DLWP-CS

25 - Machine Learning SOTA and Model Zoo

Machine Learning SOTA and Model Zoo

List of SOTA

Model Zoo

Report about AI Progress

Specific SOTA

Machine Learning Review

26 - Machine Learning Metrics

Machine Learning Metrics

Accuracy and Loss

  • Loss (not in %) can be seen as a distance between the true values of the problem and the values predicted by the model. Greater the loss is, more huge is the errors you made on the data.
  • Loss is often used in the training process to find the “best” parameter values for your model (e.g. weights in neural network). It is what you try to optimize in the training by updating weights.
  • Accuracy (in %) can be seen as the number of error you made on the data.
  • Accuracy is more from an applied perspective. Once you find the optimized parameters above, you use this metrics to evaluate how accurate your model’s prediction is compared to the true data.
  • That means :
    • a low accuracy and huge loss means you made huge errors on a lot of data
    • a low accuracy but low loss means you made little errors on a lot of data
    • a great accuracy with low loss means you made low errors on a few data (best case)

Prediction

  • Condition Positive (P) : the number of real positive cases in the data
  • Condition Negative (N) : the number of real negative cases in the data
  • True Positive or Hit
  • True Negative or Correct Rejection
  • False Positive or False Alarm or Type I error
  • False Negative or Miss or Type II error

Confusion Matrix

TP TN FP FN

TP TN FP FN

Accuracy

  • Accuracy (ACC) = (Σ True positive + Σ True negative)/Σ Total population
  • Accuracy = (TP + TN)/(TP + TN + FP + FN)
  • Accuracy is sensitive to class imbalance

Precision or Positive Predictive Value (PPV)

  • Precision measures how accurate is the predictions.
  • Precision is the percentage of the predictions are correct.
  • Precision measures the “false positive rate” or the ratio of true object detections to the total number of objects that the classifier predicted.
  • Precision: how many selected items are relevant.
  • Precision, a.k.a. positive predicted value, is given as the ratio of true positive (TP) and the total number of predicted positives.
  • Precision = Σ True positive/Σ Predicted condition positive Precision

Recall or Sensitivity or True Positive Rate or Probablity of Detection

  • Recall measures how good the algorithm find all the positives.
  • Recall measures the “false negative rate” or the ratio of true object detections to the total number of objects in the data set.
  • Recall: how many relevant items are selected.
  • Recall : the ratio of TP and total of ground truth positives.
  • Recall = Σ True positive/Σ Condition positive Recall

F1 Score

  • Harmonic mean of Precision and Recall
  • Because it is difficult to compare two models with low precision and high recall or vice versa.
  • So to make them comparable, F-Score is used.
  • F1 score = 2 · (Precision · Recall)/(Precision + Recall) Precision-Recall-F1 Formula

Matthews Correlation Coefficient (MCC) or Phi Coefficient

  • MCC is used in machine learning as a measure of the quality of binary (two-class) classifications
  • MCC takes into account all four values in the confusion matrix, and a high value (close to 1) means that both classes are predicted well, even if one class is disproportionately under- (or over-) represented. MCC

Intersection over Union (IoU)

  • IoU is used for detection algorithm

  • The IoU is given by the ratio of the area of intersection and area of union of the predicted bounding box and ground truth bounding box.

    • an IoU of 0 means that there is no overlap between the boxes
    • an IoU of 1 means that the union of the boxes is the same as their overlap indicating that they are completely overlapping

  • The IoU would be used to determine if a predicted bounding box (BB) is TP, FP or FN. The TN is not evaluated as each image is assumed to have an object in it. IoU

  • Traditionally, we define a prediction to be a TP if the IoU is > 0.5, then:

    • True Positive (if IoU > 0.5)
    • False Positive (if IoU < 0.5 or Duplicated Bounding Box)
    • False Negative, when our object detection model missed the target (if there is no detection at all or when the predicted BB has an IoU > 0.5 but has the wrong classification)

  • mean Average Precision (mAP) score is calculated by taking the mean AP over all classes and/or over all IoU thresholds. Mean average precision (mAP) is used to determine the accuracy of a set of object detections from a model when compared to ground-truth object annotations of a dataset. Note:

  • What is Mean Average Precision (mAP) in Object Detection?

  • Mean Average Precision (mAP) Explained | Paperspace Blog

  • mAP (mean Average Precision) might confuse you! | by Shivy Yohanandan | Towards Data Science

Reference

Multiclass Metrics

Accuracy, Precision, Error

  • Accuracy is closeness of the measurements to a specific value
    • More commonly, it is a description of systematic errors, a measure of statistical bias; low accuracy causes a difference between a result and a “true” value. ISO calls this trueness.
    • the accuracy of a measurement system is the degree of closeness of measurements of a quantity to that quantity’s true value
    • bias is the amount of inaccuracy
  • Precision is the closeness of the measurements to each other
    • Precision is a description of random errors, a measure of statistical variability.
    • The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.
    • variability is the amount of imprecision

Accuracy has two definitions:

  • More commonly, it is a description of systematic errors, a measure of statistical bias; low accuracy causes a difference between a result and a “true” value. ISO calls this trueness.
  • Alternatively, ISO defines accuracy as describing a combination of both types of observational error above (random and systematic), so high accuracy requires both high precision and high trueness.

Accuracy and Precision

Learn more:

  1. POCD

Machine Learning Overfitting Handling

27 - Awesome NLP Projects

Awesome NLP Projects

Learn NLP

NLP Benchmark

NLP Projects

Language NLP

Summarizer NLP

NLP

Text Generation

28 - Awesome Latex Apps

Awesome Latex Apps

Related links:
🔗 Paper and Article Search Tools
🔗 Awesome Writing Assistant Tools
🔗 Awesome Research and Academic Writing Assistant Tools
🔗 Awesome Science Blogs and Forums
🔗 Awesome Scientific Journal Tools
🔗 Awesome Conferences Tools
🔗 Awesome Latex Apps

Learn Latex

Online Paper (with Latex) Editors

Online Math (Latex) Editor

Calculator Latex

Image to Latex Converter

Sketch to Latex and Diagram

Offline Latex Editor

  • LatexDraw : LaTeXDraw is a graphical drawing editor for LaTeX. LaTeXDraw can be used to 1) generate PSTricks code; 2) directly create PDF or PS pictures.
  • Texmaker : LaTex editor
  • MiKTex : LaTex engine
  • KLatex Formula
  • SwiftLatex : WASM Latex processing

Latex Code Checker

Word to Latex

Graph to Latex

Latex to HTML

Latex Math

Latex , Markdown, and HTML

Pseudocode in Latex

29 - Awesome Scientific Journal Tools

Awesome Scientific Journal Tools

Related links:
🔗 Paper and Article Search Tools
🔗 Awesome Writing Assistant Tools
🔗 Awesome Research and Academic Writing Assistant Tools
🔗 Awesome Science Blogs and Forums
🔗 Awesome Scientific Journal Tools
🔗 Awesome Conferences Tools
🔗 Awesome Latex Apps

Impact Factor

Impact Factor for Specific Publishers

Article Processing Charge (APC) for Open Access Paper

APC and IF relationship

Journal Keyword Alert and RSS

Journal Publisher List

Journal Notes

Journal Article Tools

30 - Safety Helmet Detection

Safety Helmet and Plate Detection

Safety Helmet Detection Github Repos

License Plate Detection Github Repos

Ideas

Safety Helmet Detection Paper

Safety Helmet and Plate Detection Papers

31 - Emotion Detection with Machine Learning

Emotion Detection with Machine Learning

Github

Ideas

Library

32 - Face Mask Detection with Machine Learning

Face Mask Detection with Machine Learning

Github

Dataset

Paper

Ideas

Project in Progress (by Rozi)

  • Deep Learning for Face Detection in Real Time
  • Face Detection : SSD ResNet10 dan MTCNN
  • Mask Classification : CNN with MobileNetV2 dan VGG16Net
  • PC and Android Deployment
  • Variation :
    • distance
    • lighting
    • mask variation (+face attack)
  • Metric for Performance Analysis :
    • Accuracy, Precision, Recall, F1 for image analysis
    • mAP@0.5 (Mean Average Precision) for image analysis
    • FPS for video analysis
  • Reference:

Object Detection

  • Object Detection is Object Localization and Object Classification
  • Model for Object Detection: Fast R-CNN, Faster R-CNN, Histogram of Oriented Gradients (HOG), Region-based Convolutional Neural Networks (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot Detector (SSD), Spatial Pyramid Pooling (SPP-net), YOLO (You Only Look Once)

YOLO

TinyYolo for Mobile App

Yolo for custom object

SSD

Widerface

Model Zoo

33 - CT-Scan for Covid Classification using Machine Learning

CT-Scan for Covid-19 Classification using Machine Learning

Dataset

Notes

Github

Paper

34 - Object Detection

Object Detection

Object Detection

Object Detection and Segmentation

In-Browser Pose Identification

Rooftop Detection Machine Learning

Hand Detection and Hand Tracking

Browser based Face/Pose Identification

In-browser Object Detection

YOLO

There are a few different algorithms for object detection and they can be split into two groups:

  • Algorithms based on classification – they work in two stages. In the first step, we’re selecting from the image interesting regions. Then we’re classifying those regions using convolutional neural networks. This solution could be very slow because we have to run prediction for every selected region. Most known example of this type of algorithms is the Region-based convolutional neural network (RCNN) and their cousins Fast-RCNN and Faster-RCNN.
  • Algorithms based on regression – instead of selecting interesting parts of an image, we’re predicting classes and bounding boxes for the whole image in one run of the algorithm. Most known example of this type of algorithms is YOLO (You only look once) commonly used for real-time object detection.

YOLO metrics:

35 - Face Expression and Detection

Face Expression and Detection

36 - Awesome Google Colab Notebooks

Awesome Google Colab Notebooks

Computer Vision Google Colab Notebooks

  • Google DayDream Produce dream-alike imagery link
  • Big GAN Produce photorealistic images link
  • Style Transfer Transfer style of an image to another link
  • DeOldify Colorization of Videos link
  • 3D Ken Burns Effect 3D depth video of a photo link
  • First Order Motion model Transfers facial movements from video footage to an image link
  • WiKi Art Create Images using Art works just like Art in AI link
  • StyleGAN 2 Generate Facial Human Images using improved GAN link
  • Edge Detection Use open cv to detect edges of the various images and videos link
  • Learn to paint Teach machines to paint like human painters link
  • TwinGan Unsupervised image translation for Human portraits link
  • Face Swap GAN Takes 2 videos and swap faces link
  • Traffic Counting Counts the traffic on roads link
  • FaceBook detectron2 For object detection, covering bounding box and object instance segmentation outputs link
  • DeepFaceLab Generate High quality DeepFake Videos link
  • 3D Feature Visualization Produce feature visualizations on 3D mesh surfaces link
  • Lucid Modelzoo Visualize neural networks link
  • YOLO V3 Object detection using Yolo link
  • Clothe image classification Classify clothing images link
  • Pneumonia detection Detect pneumonia from medical x-ray images link
  • Breast cancer detection Detect breast cancer from pre-trained data sets link
  • Imaging - Amaretto Amaretto is the software toolbox for network Biology link
  • Waifu 2x Upscale photo and video quality link
  • Twitter Trends Get a list of trending news on twitter link
  • Mask R-CNN Object detection using Mask R-CNN link
  • PiFu HD 3D Human Digitalisation link
  • Sudoku Solver Solve sudoku using SatNet link
  • GPT2 Adventure GPT2 based game link
  • Atari Games Train an agent to play Atari Games link
  • Cartoon GAN Cartoonize your photos link
  • Hall of Faces Detect faces in any photo link
  • Chrome Dino Game Bot that plays chrome dino game link
  • Plant Classification A plant seedling classifier link
  • Cascade R-CNN Object detection using Cascade RCNN link
  • Pedestrian Tracking Pedestrian Tracking with YOLOv3 and DeepSORT link
  • Segment Cars & Streets Real Time Semantic Segmentation with LightNet++ link
  • LipGAN Generate lip motion link
  • Deeplab V3 Semantic Segmentation using Torchvision Deeplab v3 link
  • OpenPose Pose Detection with OpenPose link
  • DensePose Pose Detection with DensePose link
  • Text Detection Text Detection in a scene with PixelLink link
  • Face Tracker Track and Segment Persons with SiamMask link
  • Image Upsampler Upsample Images and generate HQ resolution with Pulse link
  • Super Slow motion Make any video Super SlowMo link
  • Replace Background Replace Image background with Background Matting link
  • 3D photo in-painting 3D Photography using Context-aware Layered Depth Inpainting link
  • Super Resolution Image Super Resolution Prediction link
  • 3D object detection LiDAR point cloud based 3D object detection link
  • Earth Engine Interactive mapping using earth engine API and folium link
  • OpenVINO OpenVINO Edge developement and deployment on Google Colab link
  • Autonomous Vehicle A self-driving car that manuvers itself on a two-lane track. link
  • Image Colorization Instance aware image colorization link
  • Real Image Editing In-Domain GAN Inversion for Real Image Editing link

Data Science

Jupyter Notebook

37 - Arxiv, Paper Preprint, and Curated Paper Sites

Arxiv, Paper Preprint, and Curated Paper Sites

List of Arxiv and Paper Preprint Sites

Arxiv Tools

Curated Papers

38 - Robotic Simulator

Open Source Robotic Simulator

  • Webots Open Source Robot Simulator
  • V-REP
  • Gazebo
  • ARGoS
  • OpenRave
  • Simspark
  • Drake : Drake aims to simulate even very complex dynamics of robots (e.g. including friction, contact, aerodynamics, …), but always with an emphasis on exposing the structure in the governing equations (sparsity, analytical gradients, polynomial structure, uncertainty quantification, …) and making this information available for advanced planning, control, and analysis algorithms. Drake provides an interface to Python to enable rapid-prototyping of new algorithms, and also aims to provide solid open-source implementations for many state-of-the-art algorithms.

39 - Machine Learning for Sport Pose Analysis

Machine Learning for Sport Pose Analysis

Pose Estimation

Sport Pose Analysis

Pose Estimation

Methods

  • HRNet
  • OpenPose
  • HigherHRNet
  • Smiple Baselines
  • Alphapose
  • Densepose
  • Personlab

Datasets

40 - Machine Learning for Satellite Images

Machine Learning for Satellite Images

Notes

Indonesia Landcover Maps

Open Source GIS Tools

SASPlanet

SASPlanet at SASGIS SASPlanet FAQ SASPlanet unable to load map or here The most needed links:

  1. Program (latest official release, test builds and archive of previous versions): http://sasgis.org/download/
    Attention! Servers with maps can be updated after the release, so the maps are strongly recommended to update anyway!
  2. Nightly build, separate from archive versions: http://sasgis.org/programs/sasplanet/nightly.php
  3. Program - all releases, beta versions and nightly builds (mirror): https://bitbucket.org/ sas_team / sas.planet.bin / downloads
  4. NEW merged map set: https://github.com/sasgis/sas.maps/archive/master.zip Very short instruction:
  5. unpack the program somewhere, for example, c: \ SASPlanet, but not in c: \ Program Files and not in c: \ Program Files (x86)
  6. unpack the maps into the Maps directory (since they are newer than in the archive with the program). You should get SASPlanet \ Maps \ sas.maps \ and SASPlanet \ Maps \ plus.maps \.

41 - NLP Models

NLP Models

List of Excellent NLP Pretrained Models

List Pretrained Models NLP:

NLP Course

  • CS224n : Natural Language Processing with Deep Learning

42 - NLP for Bahasa Indonesia

NLP for Bahasa Indonesia

Indonesian NLP Dataset

43 - NLP with GPT

NLP with GPT

GPT-2

GPT-3

GPT Models

GPT Notes

GPT

GPT Alternatives

NLP

44 - Digital Twin

Digital Twin in Vehicles

DT in Electric Vehicles

Digital Twin Simulation

Digital Twin Video

Digital Twin Project

45 - Awesome Jupyter Notebooks

Awesome Jupyter Notebooks

Related links: 🔗 note/Awesome Jupyter Notebooks
🔗 app/Jupyter Notebook Apps

Important contribution instructions: If you add new content, please ensure that for any notebook you link to, the link is to the rendered version using nbviewer, rather than the raw file. Simply paste the notebook URL in the nbviewer box and copy the resulting URL of the rendered version. This will make it much easier for visitors to be able to immediately access the new content.

Note that Matt Davis has conveniently written a set of bookmarklets and extensions to make it a one-click affair to load a Notebook URL into your browser of choice, directly opening into nbviewer.

Table of Contents

  1. Entire books or other large collections of notebooks on a topic
  2. Scientific computing and data analysis with the SciPy Stack
  3. General Python Programming
  4. Notebooks in languages other than Python
  5. Miscellaneous topics about doing various things with the Notebook itself
  6. Reproducible academic publications
  7. Other publications using the Notebook
  8. Data-driven journalism
  9. Whimsical notebooks
  10. Videos of IPython being used in the wild
  11. Accessing an IBM quantum computer via notebooks
  12. Software Architecture

Entire books or other large collections of notebooks on a topic

Introductory Tutorials

Programming and Computer Science

Statistics, Machine Learning, and Data Science

Mathematics, Physics, Chemistry, Biology

Earth Science and Geo-Spatial data

Linguistics and Text Mining

Engineering Education

Scientific computing and data analysis with the SciPy Stack

General topics in scientific computing

Social data

Psychology and Neuroscience

Machine Learning, Statistics and Probability

Physics, Chemistry and Biology

Economics and Finance

Earth science and geo-spatial data

Data visualization and plotting

Mathematics

Signal and Sound Processing

Natural Language Processing

Pandas for data analysis

Note that in the ‘collections’ section above there are also pandas-related links, such as the one for an 11-lesson tutorial.

General Python Programming

Notebooks in languages other than Python

These are notebooks that use [one of the IPython kernels for other languages](IPython kernels for other languages):

Julia

The IPython protocols to communicate between kernels and clients are language agnostic, and other programming language communities have started to build support for this protocol in their language. The Julia team has created IJulia, and these are some Julia notebooks:

Haskell

There exists a Haskell kernel for IPython in the IHaskell project.

OCaml

iocaml is an OCaml kernel for IPython

Ruby

Similar to the Julia kernel there exists also a Ruby kernel for IPython.

The interactive plotting library Nyaplot has some case studies using IRuby:

Perl

F#

C#

  • Xamarin Workbooks Create a rich C# workbook for Android, iOS, Mac, WPF, or Console, and get instant live results as you learn these APIs.

Javascript

Miscellaneous topics about doing various things with the Notebook itself

Reproducible academic publications

This section contains academic papers that have been published in the peer-reviewed literature or pre-print sites such as the ArXiv that include one or more notebooks that enable (even if only partially) readers to reproduce the results of the publication. If you include a publication here, please link to the journal article as well as providing the nbviewer notebook link (and any other relevant resources associated with the paper).

  1. Revealing ferroelectric switching character using deep recurrent neural networks. Github page where code is located. Jupyter Paper. Raw Data

  2. Discovery of Gravitational Waves by the LIGO collaboration. That page, from the LIGO Open Science Center, contains multiple notebooks for various datasets corresponding to different events; this binder lets you run the code right away. More details on the GW150914 event as well as the original main Physical Review Letters paper, “Observation of Gravitational Waves from a Binary Black Hole Merger”.

  3. Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow, by Brunk et al.

  4. Predicting Coronal Mass Ejections Using Machine Learning Methods by Monica Bobra and Stathis Ilonidis (Astrophysical Journal, 2016). An IPython notebook, which reproduces all the results, has been permanently deposited in the Stanford Digital Repository.

  5. The Paper of the Future by Alyssa Goodman et al. (Authorea Preprint, 2017). This article explains and shows with demonstrations how scholarly “papers” can morph into long-lasting rich records of scientific discourse, enriched with deep data and code linkages, interactive figures, audio, video, and commenting. It includes an interactive d3.js visualization and has an astronomical data figure with an IPYthon Notebook “behind” it.

  6. Reply to ‘Influence of cosmic ray variability on the monsoon rainfall and temperature’: a false-positive in the field of solar-terrestrial research by Benjamin Laken, 2015. Reviewed article will appear in JASTP. The IPython notebook reproduces the full analysis and figures exactly as they appear in the article, and is available on Github: link via figshare.

  7. An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study, by Zichen Wang and Avi Ma’ayan. (F1000Research 2016, 5:1574). An IPython notebook was used to perform the proposed RNA-Seq pipeline using public gene expression data of human cells after Zika virus infection. The computational pipeline is also version controlled and Dockerized available here.

  8. The probability of improvement in Fisher’s geometric model: a probabilistic approach, by Yoav Ram and Lilach Hadany. (Theoretical Population Biology, 2014). An IPython notebook, allowing figure reproduction, was deposited as a supplementry file.

  9. Stress-induced mutagenesis and complex adaptation, by Yoav Ram and Lilach Hadany (Proceedings B, 2014). An IPython notebook, allowing figures reproduction, was deposited as a supplementry file.

  10. Automatic segmentation of odor maps in the mouse olfactory bulb using regularized non-negative matrix factorization, by J. Soelter et al. (Neuroimage 2014, Open Access). The notebook allows to reproduce most figures from the paper and provides a deeper look at the data. The full code repository is also available.

  11. Multi-tiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss, by A. Gross et al. (Nature Genetics 2014). The full collection of notebooks to replicate the results.

  12. Dog and human inflammatory bowel disease rely on overlapping yet distinct dysbiosis networks, by Vázquez-Baeza et al. (Nature microbiology 2016). The full collection of notebooks to replicate the results.

  13. powerlaw: a Python package for analysis of heavy-tailed distributions, by J. Alstott et al.. Notebook of examples in manuscript, ArXiv link and project repository.

  14. Collaborative cloud-enabled tools allow rapid, reproducible biological insights, by B. Ragan-Kelley et al.. The main notebook, the full collection of related notebooks and the companion site with the Amazon AMI information for reproducing the full paper.

  15. A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data, by C.T. Brown et al.. Full notebook, ArXiv link and project repository.

  16. The kinematics of the Local Group in a cosmological context by J.E. Forero-Romero et al.. The Full notebook and also all the data in a github repo.

  17. Warming Ocean Threatens Sea Life, an article in Scientific American backed by a notebook for its main plot. By Roberto de Almeida from MarinExplore.

  18. Extrapolating Weak Selection in Evolutionary Games, by Wu, García, Hauert and Traulsen. PLOS Comp Bio paper and Figshare link.

  19. Using neural networks to estimate redshift distributions. An application to CFHTLenS by Christopher Bonnett paper(submitted to MNRAS)

  20. Mechanisms for stable, robust, and adaptive development of orientation maps in the primary visual cortex by Jean-Luc R. Stevens, Judith S. Law, Jan Antolik, and James A. Bednar. Journal of Neuroscience, 33:15747-15766, 2013. [Notebook1] (https://ioam.github.io/topographica/_static/gcal_notebook.html), Notebook2.

  21. Accelerated Randomized Benchmarking, by Christopher Granade, Christopher Ferrie and D. G. Cory. New Journal of Physics 17 013042 (2015), arXiv, GitHub repo.

  22. Dynamics and associations of microbial community types across the human body, by Tao Ding & Patrick D. Schloss. Notebook replicating results.

  23. Variations in submarine channel sinuosity as a function of latitude and slope, by Sylvester, Z., Pirmez, C., Cantelli, A., & Jobe, Z. R.

  24. Frontoparietal representations of task context support the flexible control of goal directed cognition, by M.L. Waskom, D. Kumaran, A.M. Gordon, J. Rissman, & A.D. Wagner. Github repository | Main notebook

  25. pyparty: Intuitive Particle Processing in Python, Adam Hughes Notebook to Generate the Published Figures | Also, check out the pyparty tutorial notebooks.

  26. Indication of family-specific DNA methylation patterns in developing oysters, Claire E. Olson, Steven B. Roberts doi: https://dx.doi.org/10.1101/012831. Notebook to generate results in the paper.

  27. Parallel Prefix Polymorphism Permits Parallelization, Presentation & Proof, Jiahao Chen and Alan Edelman, HPTCDL'14. Website and notebook

  28. Transcriptome Sequencing Reveals Potential Mechanism of Cryptic 3’ Splice Site Selection in SF3B1-mutated Cancers by Christopher DeBoever et al. There are several notebooks to replicate results and make figures.

  29. A Workflow for Characterizing Nanoparticle Monolayers for Biosensors: Machine Learning on Real and Artificial SEM Images, Adam Hughes, Zhaowen Liu, Maryam Raftari, Mark. E Reeves. Notebooks are linked in Table 1 in the text.

  30. AtomPy: An Open Atomic Data Curation Environment for Astrophysical Applications, by C. Mendoza, J. Boswell, D. Ajoku, M. Bautista.

  31. Visualizing 4-Dimensional Asteroids, in Scientific American (by Jake VanderPlas)

  32. Challenges and opportunities in understanding microbial communities with metagenome assembly, accompanied by IPython Notebook tutorial, by Adina Howe and Patrick Chain.

  33. Structure of a shear-line polar low (2016) by Sergeev, D. E., Renfrew, I. A., Spengler, T. and Dorling, S. R. Q.J.R. Meteorol. Soc. doi:10.1002/qj.2911. Accompanied by Notebooks to generate the published figures.

  34. Detecting High-Order Epistasis in Nonlinear Genotype-Phenotype Maps by Zachary R. Sailer and Michael J. Harms published in Genetics, March 2017 . All figures can be reproduced by the set of notebooks in this Github repo.

  35. Summary Analysis of the 2017 GitHub Open Source Survey by Stuart Geiger. Preprint in SocArXiv, June 2017. doi:10.17605/OSF.IO/ENRQ5. Paper is derived from a notebook converted to LaTeX with nbconvert. Notebook and materials at: OSF, GitHub, nbviewer

  36. The weirdest SDSS galaxies: results from an outlier detection algorithm, by D. Baron and D. Poznanski. Notebooks to replicate.

  37. Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data, by Nicolas Fernandez et al. Notebooks: Fig. 3, Fig. 4, Fig. 5

  38. Sociology: An investigation of Social Class Inequalities in General Cognitive Ability in Two British Birth Cohorts. Preprint in SocArXiv, December 2017. doi: 10.17605/OSF.IO/SZXDM. Notebook and materials at: OSF, GitHub, nbviewer.

  39. An on-chip architecture for self-homodyned nonclassical light, quant-ph ArXiV preprint, Nov 2016, by Fischer et al. A supporting notebook for all calculations included in the ArXiV submission.

  40. A nested sampling code for targeted searches for continuous gravitational waves from pulsars, gr-qc ArXiV preprint, May 2017, by Pitkin et al. Complete repo with supporting notebooks and sources on GitHub.

  41. HyperTools: A Python toolbox for visualizing and manipulating high-dimensional data, stat.OT ArXiV preprint by Heusser et al. A repo with companion notebooks is available, that links to the library itself, HyperTools.

  42. Nonsinusoidal beta oscillations reflect cortical pathophysiology in Parkinson’s disease, in Journal of Neuroscience by Cole et al. A repo with companion notebooks with all necessary data is available to reproduce all figures.

  43. Cycle-by-cycle analysis of neural oscillations, in bioRxiv by Cole & Voytek. A repo with companion notebooks with all necessary data is available to reproduce all figures. This repo also links to the related useful library, neurodsp, which contains notebooks of tutorials.

  44. pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage, a preprint by S. Bonaretti et al. Jupyter notebooks are used as a graphical user interface for medical image processing and analysis. The paper is interactive, with links to data, software, and documentation throughout the text. Every figure caption contains links to fully reproduce graphs.

Data-driven journalism

Whimsical notebooks

Videos of IPython being used in the wild

Of course the first thing you might try is searching for videos about IPython (1900 or so by last count on Youtube) but there are demonstrations of other applications using the power of IPython but are not mentioned is the descriptions. Below are a few such:

Accessing and programing a IBM quantum computer via notebooks

  • Github notebook example (scroll down) illustrating how to use Qiskit and access the IBMQ quantum computers.

Software Architecture

46 - NCBI Papers with Code

NCBI Papers with Code

List of awesome NCBI Papers with Code Supplement.

CNN

  1. Dual CNN for Relation Extraction with Knowledge-Based Attention and Word Embeddings Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6664687/ Code: https://github.com/mrlijun2017/Dual-CNN-RE

  2. CNN-BLPred: a Convolutional neural network based predictor for β-Lactamases (BL) and their classes Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751796/ Code: https://github.com/whiteclarence/CNN-BLPred

  3. Design of deep convolutional networks for prediction of image rapid serial visual presentation events Paper: https://www.ncbi.nlm.nih.gov/pubmed/29060296 Code: https://github.com/ZijingMao/ROICNN

  4. A simple convolutional neural network for prediction of enhancer-promoter interactions with DNA sequence data Paper: https://www.ncbi.nlm.nih.gov/pubmed/30649185 Code: https://github.com/zzUMN/Combine-CNN-Enhancer-and-Promoters

  5. A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition Paper:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6207326/ Code: https://github.com/biopatrec/biopatrec

  6. GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text Paper: https://www.ncbi.nlm.nih.gov/pubmed/29272325 Code: https://github.com/valdersoul/GRAM-CNN

  7. Simple tricks of convolutional neural network architectures improve DNA-protein binding prediction Paper: https://www.ncbi.nlm.nih.gov/pubmed/30351403 Code: https://github.com/zhanglabtools/DNADataAugmentation

  8. EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5937476/ Code: https://github.com/shervinea/enzynet

  9. Multi-timescale drowsiness characterization based on a video of a driver’s face Paper: https://www.telecom.ulg.ac.be/mts-drowsiness/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165048/ Code: https://github.com/QMassoz/mts-drowsiness

  10. CLoDSA: a tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentation tasks Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6567576/ Code: https://github.com/joheras/CLoDSA

  11. Deep learning with convolutional neural networks for EEG decoding and visualization Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5655781/ Code: https://github.com/robintibor/braindecode/ Code: https://github.com/TNTLFreiburg/braindecode

Rice/Paddy Classification

  1. Classifying Oryza sativa accessions into Indica and Japonica using logistic regression model with phenotypic data Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6842562/ Code: https://github.com/bongsongkim/logit.regression.rice

  2. SNNRice6mA: A Deep Learning Method for Predicting DNA N6-Methyladenine Sites in Rice Genome Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797597/ Code: https://github.com/yuht4/SNNRice6mA

  3. Automatic estimation of heading date of paddy rice using deep learning Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6626381/ Code: https://github.com/svdesai/heading-date-estimation

  4. Distillation of crop models to learn plant physiology theories using machine learning Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6541271/ Code: https://github.com/ky0on/simriw

  5. Evaluating remote sensing datasets and machine learning algorithms for mapping plantations and successional forests in Phnom Kulen National Park of Cambodia Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6814064/ Code: https://github.com/Jojo666/PKNP-Data

  6. PlantCV v2: Image analysis software for high-throughput plant phenotyping Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5713628/ Code: https://github.com/danforthcenter/plantcv-v2-paper

  7. Crop Yield Prediction Using Deep Neural Networks Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6540942/ Code: https://github.com/saeedkhaki92/Yield-Prediction-DNN

  8. Using Deep Learning for Image-Based Plant Disease Detection Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5032846/ Code: https://github.com/salathegroup/plantvillage_deeplearning_paper_analysis

  9. Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5500639/ Code: https://github.com/p2irc/deepplantphenomics

  10. DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6375952/ Code: https://github.com/AlexOlsen/DeepWeeds