✍🏼 Generate handwriting with an in-browser recurrent neural network!

Web demo is available here: https://seanvasquez.com/handwriting-generation/

The model is based on Alex Graves’ 2013 paper on handwriting generation - https://arxiv.org/abs/1308.0850, but adds an inference model to allow for sampling latent style vectors (similar to the VAE model used by SketchRNN - https://arxiv.org/abs/1704.03477). The web demo is implemented with tensorflow.js

Check out Pose Animator created by Shan Huang - A web animation tool that brings SVG illustrations to life with real time human perception TF.js models.

Play with demos: https://pose-animator-demo.firebaseapp.com

GitHub (with more documentation about how it works): https://github.com/yemount/pose-animator/

The anatomy of a vanilla JavaScript plugin | Go Make Things
https://gomakethings.com/the-anatomy-of-a-vanilla-javascript-plugin/

Building Your Own JavaScript Modal Plugin ― Scotch.io
https://scotch.io/tutorials/building-your-own-javascript-modal-plugin

Kaggle Faker News Classifier Using LSTM- Deep LEarning| Natural ...
https://www.youtube.com/watch?v=MXPh_lMRwAI

YouTube playlist for Data Science and AI, that you must check:

https://www.linkedin.com/pulse/top-youtube-playlist-data-science-ai-you-must-check-mamdapure

Resources discussed during our LIVE Session on Instagram today:

Book: Artificial Intelligence - a modern approach.
PDF (older edition): https://www.cin.ufpe.br/~tfl2/artificial-intelligence-modern-approach.9780131038059.25368.pdf
Amazon link to the newest edition: http://amzn.to/2Xi19YL

Courses:
Andrew Ng's course: https://www.coursera.org/learn/machine-learning
IBM's course: https://www.coursera.org/professional-certificates/ibm-data-science

Connect with Nidhi, our guest on the live session on: https://www.linkedin.com/in/nidhithakkar55

Connect with Priyanka, MLI's founder on: https://www.linkedin.com/in/priyanka-kasture-7aaaab75

Follow MLI on LinkedIn: https://www.linkedin.com/company/mlindia/

Follow us on Instagram to stay updated about more of our LIVE sessions: instagram.com/ml.india/

Happy machine learning! 😃

Curious how you can convert already trained traditional ML models into tensor computations with up to 100x speedup?

Check out this blog post from Microsoft for details: https://azuredata.microsoft.com/articles/ebd95ec0-1eae-44a3-90f5-c11f5c916d15

Source code: https://github.com/microsoft/hummingbird

Learn AI/NLP and Machine Learning needed and deploy your own AI based Intelligent Chatbot.

Also learn how to earn cash after learning to build your own chatbot.

Kickstart your chatbot journey with our 6 Days Workshop starting from 12th June.

Participants will also get an AI Chatbot Cheetsheet and all the recorded sessions in free from theMAD.

The revolution of bots taking shape!

Are you part of this revolution?

Enroll now in just Rs 16 per day.

🔗 Link for enroll is here...

https://allevents.in/online/80002220391294


Other details on the above link 👆

Link : http://t.me/wethemadians

​​CREME – python library for online ML

All the tools in the library can be updated with a single observation at a time, and can therefore be used to learn from streaming data.

The model learns from one observation at a time, and can therefore be updated on the fly. This allows to learn from massive datasets that don't fit in main memory. Online machine learning also integrates nicely in cases where new data is constantly arriving. It shines in many use cases, such as time series forecasting, spam filtering, recommender systems, CTR prediction, and IoT applications. If you're bored with retraining models and want to instead build dynamic models, then online machine learning might be what you're looking for.

Here are some benefits of using creme (and online machine learning in general):
• incremental – models can update themselves in real-time
• adaptive – models can adapt to concept drift
• production-ready – working with data streams makes it simple to replicate production scenarios during model development
• efficient – models don't have to be retrained and require little compute power, which lowers their carbon footprint


api reference: https://creme-ml.github.io/content/api.html
github: https://github.com/creme-ml/creme

What Is Data Preparation in a Machine Learning Project
https://machinelearningmastery.com/what-is-data-preparation-in-machine-learning/