- Pet Rock
Machine Learning: Blog
[Rahmadya Trias Handayanto "Just for a little kindness"](https://rahmadya.com/)
Machine Learning: Image Generator
Scientific Report Tools
- Weave.jl - Scientific Reports Using Julia · Weave.jl
- R Markdown
- Pweave - Scientific Reports Using Python — Pweave v0.30 documentation
[knitr - Elegant, flexible, and fast dynamic report generation with R - Yihui Xie 谢益辉](https://yihui.org/knitr/)
Time Tracker and Productivity
[Opinion They Stormed the Capitol. Their Apps Tracked Them. - The New York Times](https://www.nytimes.com/2021/02/05/opinion/capitol-attack-cellphone-data.html)
Visualization: Neural Network
- Complexity Explained
[Complexity Explorables Complexity Explorables](https://www.complexity-explorables.org/)
Web-based Local Only Editor
- Hush Docs
Spartan Web Selfhosted
Android Machine Learning
- MLKIt Android
- A Look at Android ML Kit - the Machine Learning SDK
- asmaamirkhan/MLKitDemo: A demo app for various Firebase ML-Kit vision functionalities 🕵️♀️
- googlesamples/mlkit: A collection of sample apps to demonstrate how to use Google's ML Kit APIs on Android and iOS
- anderspitman/awesome-tunneling: List of ngrok alternatives and other ngrok-like tunneling software and services. Focus on self-hosting.
- Visual guide to SSH tunnels
- With YunoHost, you can easily manage a server for your friends, association or enterprise. Learn more • YunoHost
- FreedomBox - Personal Server at Home
Machine Learning Visualization
- Sounds For Sleep
- Ambient rain simulator for pluviophiles. Relaxing sound of rain.
[Defonic A fabulous ambient noise generator](https://defonic.com/)
- Online Background Noises • Relaxing Sounds • Noise Masking
- A Soft Murmur
- Custom SoundPad - Tabletop Audio
Machine Learning: Action Recogntion
- TwentyBN/sense: Enhance your application with the ability to see and interact with humans using any RGB camera.
[Vector Pattern Generator VISIWIG](https://www.visiwig.com/patterns/)
- Free to use Illustrations by ManyPixels
- LogoSear.ch: Instant logo search for SVG logos
- SVG Repo - Free SVG Vectors and Icons
- SVG Can Do That?
- deepjazz: deep learning for jazz jisungk/deepjazz: Deep learning driven jazz generation using Keras & Theano!
- KarthikNayak/DeepRock: Rock Music using Deep Learning
- salu133445/musegan: An AI for Music Generation reddit
- Music Transformer: Generating Music with Long-Term Structure
- Generative Deep Learning for Virtuosic Classical Music: Generative Adversarial Networks as Renowned Composers : MachineLearning
- Real-time Performance RNN in the Browser
- GRUV: Algorithmic Music Generation using Recurrent Neural Networks - Aran Nayebi
[Wikipedia Pageviews Analysis](https://pageviews.toolforge.org/?project=en.wikipedia.org&platform=all-access&agent=user&redirects=0&range=latest-20&pages=Cat Dog)
- Preface - 100 Page Python Intro License: CC-BY-NC
- Preface - Python resources for everybody License: CC
Python by Projects
- norvig/pytudes: Python programs, usually short, of considerable difficulty, to perfect particular skills.
- karan/Projects-Solutions: Links to others' solutions to Projects (https://github.com/karan/Projects/)
- tuvtran/project-based-learning: Curated list of project-based tutorials
[Code with Repl.it Python projects for beginners](https://www.codewithrepl.it/)
[FarmBot Open-Source CNC Farming](https://farm.bot/)
- conwnet/github1s: One second to read GitHub code with VS Code.
- Just add 1s after github and press Enter in browser address bar for any repository you want to read.
In-browser Object Detection
- In-Browser object detection using YOLO and TensorFlow.js - DEV Community 👩💻👨💻
- ModelDepot/tfjs-yolo-tiny: In-Browser Object Detection using Tiny YOLO on Tensorflow.js
- In-Browser object detection using YOLO and TensorFlow.js - Questions - Community - Synthiam
[Build a Realtime Object Detection Web App in 30 Minutes by Erdem Isbilen Towards Data Science](https://towardsdatascience.com/build-a-realtime-object-detection-web-app-in-30-minutes-7ad0cb2231fb)
[Build Custom Object Detection Web Application Using TensorFlow.js by Kosta Malsev The Startup Jan, 2021 Medium](https://medium.com/swlh/build-custom-object-detection-web-application-using-tensorflow-js-d1664f96a18b)
- 🤖 Object Detection using Tensorflow.js - Tutorial
- Custom object detection in the browser using TensorFlow.js — The TensorFlow Blog
- Object Detection and Image Classification with YOLO - KDnuggets : YOLO is based on regression not classification
[Real-time Object Detection with YOLO, YOLOv2 and now YOLOv3 by Jonathan Hui Medium](https://jonathan-hui.medium.com/real-time-object-detection-with-yolo-yolov2-28b1b93e2088)
- LeonLok/Multi-Camera-Live-Object-Tracking: Multi-camera live traffic and object counting with YOLO v4, Deep SORT, and Flask.
- burningion/poor-mans-deep-learning-camera: Build a thin client deep learning camera with the Raspberry Pi, Flask, and YOLO
- theAIGuysCode/Object-Detection-API: Yolov3 Object Detection implemented as APIs, using TensorFlow and Flask
- v-iashin/WebsiteYOLO: The back-end for YOLOv3 object detector running online on my website
- Zyjacya-In-love/Pedestrian-Detection-on-YOLOv3_Research-and-APP: 2020 Undergraduate Graduation Project in Jiangnan University ALL codes including Data-convert, keras-Train, model-Evaluate and Web-App
- yankai364/Object-Detection-Flask-API: A simple YOLOv3 object detection API in Python (using Flask).
- How to Easily Deploy Machine Learning Models Using Flask - KDnuggets : show text results
- Turning Machine Learning Models into APIs - DataCamp
- Deploy Machine Learning Model using Flask - GeeksforGeeks
- Deployment of Machine learning models using Flask - KDnuggets
[Tutorial: Deploying a machine learning model to the web by Cambridge Spark Cambridge Spark](https://blog.cambridgespark.com/deploying-a-machine-learning-model-to-the-web-725688b851c7)
[Python Machine Learning and Predicting With Flask Toptal](https://www.toptal.com/python/python-machine-learning-flask-example)
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.
- whynotw/YOLO_metric: Calculate mean Average Precision (mAP) and confusion matrix for object detection models. Bounding box information for groundtruth and prediction is YOLO training dataset format.
- rafaelpadilla/review_object_detection_metrics: Review on Object Detection Metrics: 14 object detection metrics including COCO's and PASCAL's metrics. Supporting different bounding box formats.
[Evaluating Object Detection Models: Guide to Performance Metrics Manal El Aidouni](https://manalelaidouni.github.io/Evaluating-Object-Detection-Models-Guide-to-Performance-Metrics.html)
In-Browser Pose Identification
- 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:
- One-Shot Medical Image Segmentation: All you need is one labeled image
[Dense cellular segmentation for EM using 2D–3D neural network ensembles Scientific Reports](https://www.nature.com/articles/s41598-021-81590-0)
[Dense cellular segmentation for EM using 2D-3D neural network ensembles leapmanlab.github.io](https://leapmanlab.github.io/dense-cell/)
Deep Learning : Review on Review
- DL Surveyception: a survey of overviews, reviews and surveys in deep learning for computer vision, time-series and others - HackMD
- Every Time Zone: time zone converter, compare time zone difference and find best time for a meeting with one click
Machine Learning Blog
[SVG Pattern Generator Create Seamless, Royalty-Free Patterns.](https://doodad.dev/pattern-generator/)
[Dither Me This Image Dithering Tool](https://doodad.dev/dither-me-this/)
- Computer Networks: A Systems Approach — Computer Networks: A Systems Approach Version 6.2-dev documentation License: CC-BY
Rooftop Detection Machine Learning
[Improved Rooftop Detection in Aerial Images with Machine Learning SpringerLink](https://link.springer.com/article/10.1023/A:1025623527461)
- Novel Approach for Rooftop Detection Using Support Vector Machine
- Deep-learning: Rooftop type detection with Keras and TensorFlow – fractalytics
[Increasing Solar adoption in the developing world through Machine Learning image segmentation by Rudradeb Mitra Towards Data Science](https://towardsdatascience.com/using-image-segmentation-to-identify-rooftops-in-low-resolution-satellite-images-c791975d91cc)
- Shotcut - Home
[OpenShot Video Editor Free, Open, and Award-Winning Video Editor for Linux, Mac, and Windows!](https://www.openshot.org/)
Single HTML Web
- pseudosavant/player.html: One file drop-in video player web app for using video files served using basic directory listing
- pseudosavant/markdown.html: Browse an HTTP folder and view markdown or any other text document
[Gradio Machine Learning Hosting](https://gradio.app/hub)
[Gradio Introducing Hosted](https://gradio.app/introducing-hosted)
- hendrycks/natural-adv-examples: A Harder ImageNet Test Set
Cell Tower Dataset
- Cellular Tower and Signal Map
- OpenCelliD - Largest Open Database of Cell Towers & Geolocation - by Unwired Labs
AI for Humanity
- Applying Artificial Intelligence to Identify Malaria Infected Water Bodies
- How Omdena AI Challenges Work
AI for Audio
Machine Learning Course
- StanfordVL/cs131_notes: Class notes for CS 131.
[Machine Learning Crash Course Google Developers](https://developers.google.com/machine-learning/crash-course)
[adversarial.io – Fighting mass image recognition](https://adversarial.io/)
[All Courses lynda.com](https://www.lynda.com/allcourses)
- parrt/msds621: Course notes for MSDS621 at Univ of San Francisco, introduction to machine learning Introduction to Machine LEarning
[Data Science Weekly Newsletter Archive Data Science Weekly](https://www.datascienceweekly.org/newsletters)
- Workers or self-employed?
- The court made very specific arguments:
- Uber set the fare which meant that they dictated how much drivers could earn
- Uber set the contract terms and drivers had no say in them ‘
- Request for rides is constrained by Uber who can penalise drivers if they reject too many rides
- Uber monitors a driver's service through the star rating and has the capacity to terminate the relationship if after repeated warnings this does not improve
- The main difference I see is the imbalance of power. A self-employed can decide who to work with and negotiate almost everything with them. He have my business, the company have theirs, and they discuss to try to reach agreements or not. Uber drivers are not in this position. Uber can dictate everything they want, drivers can only decide to stay or leave.
- The issue here is that Uber pretends the driver is contracted by the passenger. In which case the driver should be free to negotiate with the passenger. That Uber exercises control of the relationship is what is indicating that Uber is an employer here. Uber pretends you're working for the customer, not them. That is a key element in their attempt to pretend they're not an employer.
- Uber acts as an intermediary party between the contractor and the client (the passenger), so you should compare it to platforms for contract work, such as Upwork, or fiverr, or something like that. The point of Uber is that they're only an intermediate party that puts clients and drivers together. Drivers are not providing a service to Uber. They don't control your rates, they don't stop you from getting jobs if you reject a lot, they don't set any quality minimums for your job, and they don't remove you if someone doesn't give you five stars. In other words, you're not working for these platforms, it's just a marketplace. With Uber, drivers are working for Uber, not for the passengers. However, Uber is pretending to be a marketplace, and it isn't. This sentence just proves that.
Blog with AI
[fastpages An easy to use blogging platform with support for Jupyter Notebooks.](https://fastpages.fast.ai/)
- fastai/fastpages: An easy to use blogging platform, with enhanced support for Jupyter Notebooks.
Example: [My First NN Part 3. Multi-Layer Networks and Backpropagation Scott H. Hawley](https://drscotthawley.github.io/blog/2019/02/08/My-1st-NN-Part-3-Multi-Layer-and-Backprop.html)
[Data science notebooks in Review 2020 by Elizabeth Dlha Deepnote Deepnote](https://medium.com/deepnote/data-science-notebooks-2020-review-15a3c95cfc09)
[GuitarML Zak Jost](https://blog.zakjost.com/post/guitarml/) Audio Enhancement Machine Learning
- qeeqbox/social-analyzer: API, CLI & Web App for analyzing & finding a person's profile across 350+ social media websites (Detections are updated regularly)
[The Collaborative Data Science Platform Mode](https://mode.com/)
[RapidMiner Best Data Science & Machine Learning Platform](https://rapidminer.com/)
[Machine Learning and RapidMiner Tutorials RapidMiner Academy](https://academy.rapidminer.com/)
[Online Certification RapidMiner Academy](https://academy.rapidminer.com/pages/certification)
Web Scraping dan Data Mining
[Data Science and Cognitive Computing Courses - Cognitive Class Free Courses in Data Science, AI, Cloud Computing, Containers, Kubernetes, Blockchain and more.](https://cognitiveclass.ai/)
- Moodle Activity Reseources
- Activity Types in Moodle
- How to Create an Activity in Moodle the Smart Way!
- Moodle Activities
- 47 Interesting Ways to Use Moodle to Support Learning
- EduTech Wiki
- VHDL - FPGA Tutorial
- Project F - FPGA Development
- Verilog, Formal Verification and Verilator Beginner's Tutorial
- fpga4fun.com - where FPGAs are fun
- Thomas Megel's OpenScan Offers 10-Micron 3D Scanning on a Raspberry Pi and HQ Camera Module - Hackster.io
- IoTaWatt™ Open WiFi Electric Power Monitor
[emonPi - Guide OpenEnergyMonitor](https://guide.openenergymonitor.org/technical/emonpi/)
- MyHeritage Deep Nostalgia™, deep learning technology to animate the faces in still family photos - MyHeritage
- AliaksandrSiarohin/first-order-model: This repository contains the source code for the paper First Order Motion Model for Image Animation
- First order model