ML Machine Learning releases

Image Recognition (10 Videos):

Deep / Machine learning resources:

External Links:

Clarifai Reference:

Forum and Help:

Step by step how to install python, clarify and run a demo code

  1. Download & Install Python – Just download and install 2.X
  2. Windows
    • Add to system path:
      • c:\python27\
      • c:\python27\scripts\
  3. Run example code of Python
    • print “Hello World”
  4. Open command prompt and run the following:
    • pip install clarifai==2.0.28
  5. Install guide for clarifai
  6. After a successful install without error messages, do the following:
    1. change directory to c:\python27\scripts
    2. At command prompt run the following: python clarifai config
  7. Provide clarifai_app_id and clarifai app_secret as follows:
    • Under your clarifai account use or create new app. Find HIDE LEGACY AUTHORIZATION
    • The app ID and Secret will be there.
  8. Login to your clarifai account
  9. Select PREDICT from the following page:
  10. Copy and paste the following to a python file:
from import ClarifaiApp

app = ClarifaiApp("{client_id}", "{client_secret}")

# get the general model
model = app.models.get("general-v1.3")

# predict with the model

Steps to install:

  • Install Ubuntu 16.04 lts
  • Open terminal and install GIT:
    • sudo apt-get update
    • sudo apt-get install git
  • Clone Darknet repo
    • git clone
    • cd darknet
    • make
    • ./darknet
    • You suppose too get hte following output: usage: ./darknet
  • Install OpenCV
    • sudo apt-get install libopencv-dev python-opencv
    • Edit make file and change to OPENCV=1
    • Make again
    • Test by the following command line:
      • ./darknet imtest data/eagle.jpg

How to test Darknet?

  • At terminal, at darknet directory write:
    • ./darknet
      • Result: usage: ./darknet
  • How to test OpenCV?
    • Open terminal and paste:
      • ./darknet imtest data/eagle.jpg
      • A window with eagle picture will appear.
  • How to test darknet predict and classify?
    • Download Extractions.weights. 
    • Open terminal and paste:
      • ./darknet classifier predict cfg/ cfg/extraction.cfg extraction.weights data/eagle.jpg
        • Result: classification of image.
  • How to test Yolo?
    • Download Yolo.weights
    • Open terminal and paste:
      • ./darknet detect cfg/yolo.cfg yolo.weights data/dog.jpg
        • Result: image with classification

Darknet Website

  • Link:
  • Live video:
    • ./darknet detector demo cfg/ cfg/yolo.cfg yolo.weights
  • Video from file:
    • ./darknet detector demo cfg/ cfg/yolo.cfg yolo.weights 
  • Changing The Detection Threshold
    • ./darknet detect cfg/yolo.cfg yolo.weights data/dog.jpg -thresh 0


  • Pull darknet
    • sudo docker pull loretoparisi/darknet
  • Test Docker darknet:
    • run darknet
      • docker run –rm -it –name darknet loretoparisi/darknet bash
      • cd darknet
      • ./darknet detector test cfg/ cfg/yolo.cfg /root/yolo.weights data/dog.jpg
  • AI
  • Machine learning
  • Tensorflow
  • Deep learning
  • Generate models for tensorflow
  • Microsoft application for machine learning
Posted in AI.


  • A home that has elements that can be controlled by Voice and / or smartphone app.
  • What elements are controlled?
    • Lights
    • Hot water boiler
    • Air conditioner
    • TV
    • All IR controlled elements
    • Many many more
  • The next step of evolution for smart-home will be to integrate with Google Home.
  • What you will achieve with Google home (google play / app store)?
    • Share with other home members the ability to control devices with having them to install 3rd party apps.
    • Voice control over the devices.
  • Configure your devices by eWelink sonoff or by broadlink.
  • Add devices to google home and link accounts of Sonoff and Broadlink to google home.
  • Vocie control by smartphone or by google home app the devices at your home.
  • Note for Broadlink:
    • In order to integrate with Google home, user must use “Intelligent home control” – IHC app.
    • At least one scene must be defined at IHC app in order to be able to control the device.
    • How to operate the device by voice?
      • At google home state:
        • “Hey google” (or “OK google”) “turn on” (or “switch on”) [scene name at IHC] .
  • How to connect
  • Step 1 – wiring as follows:
  • Step 2 – Long click on the button of the Sonoff
    • Device will enter setup mode.
  • Step 3 – Configure Ewelink app with sonoff device.