Connect with random people instantly. Find them in the world’s largest group chat. The “Omegle” for people who don’t want to get creepy messages from old people and weird strangers! Free private chat forever, and meet people along the way. Zonish is also great for you to contact your friends anonymously. is also the best way to contact your friends anonymously, without your parents finding out! Our site is pretty much a way for you to launder your chats. Statistically, the chance of someone finding your chat is impossible, unless they are with you in real life, looking at your computer or device. We hope to make the internet a safer and more secure place for everyone to chat on, without the risks of being spied on, by anyone untrustworthy. Talking to strangers online can be sketchy, so if you are ever talking to someone you don’t feel comfortable with, please just leave the chat. If you are reading this, please let us know if you have any ideas, questions, or concerns for our website here: [email protected] Thanks for reading and enjoy chatting!

Build an image classification model


This code pattern explains how to classify an American Sign Language (ASL) alphabet using PyTorch and deep learning networks. It uses a pretrained model from the PyTorch models zoo and retrains the last part of the network.


The code pattern uses PyTorch to build and train a deep learning model to classify images to 29 classes (26 ASL alphabet, space, Del, and nothing), which can be used later to help hard-of-hearing people communicate with others as well as with computers. The pattern uses a pretrained mobile network, defines a classifier, and connects it to network. It then trains this classifier along with some of the last blocks of the network on the data set. The pattern uses the Python and GPU environment in IBM® Watson™ Studio for faster training, which allows you to download, explore, build, and train your model. Learn more about available Watson Studio environments.

After completing this pattern, you understand how to:

  • Obtain a data set from Kaggle
  • Explore data and define transformers to preprocess images before training
  • Define a classifier to have an output layer of 29 outputs
  • Train the last blocks of the network along with the classifier that’s defined
  • Test the trained model



  1. Log in to Watson Studio.
  2. Get your Kaggle API credentials.
  3. Run the Jupyter Notebook in Watson Studio.


Get detailed steps in the readme file. Those steps show how to:

  1. Sign up for Watson Studio.
  2. Create a new project.
  3. Create the notebook.
  4. Run the notebook.
  5. Test your model.