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Hi there 👋

  • 🤔 Something that puzzles me - 'What Machine Learning can achieve? Without human aid?'. Formulating any ML problem is a hard job. Can we allow the computer itself to figure out 'how to formulate the problem?' . It sounds silly. But, if you look closely, data scientists/ ML engineers have to spend hours to formulate any machine learning problem into mathematical notion (which we called Objective Functions). And, the computer does the rest of the work → finding the optimal parameters to fit the data or discover the pattern. What if computer itself formulate the notion of problem in machine understandable objective or goal?
  • 💬 I enjoy discussing challenges of machine learning application development.
  • 📫 How to reach me: LinkedIn, Twitter
  • ⚡ Fun fact: A jellyfish is 95% water

Sijan Bhandari's Projects

autonet icon autonet

Dynamic network creation for time series

awesome-nlp icon awesome-nlp

:book: A curated list of resources dedicated to Natural Language Processing (NLP)

darts icon darts

A python library for user-friendly forecasting and anomaly detection on time series.

django-autoslug-field icon django-autoslug-field

AutoSlugField for Django based on django-extensions AutoSlugField, adds option to track parent field for slug.

django-nvd3 icon django-nvd3

Django wrapper for nvd3 - It's time for beautiful charts

djangosnippets.org icon djangosnippets.org

The code that powers djangosnippets.org, it allows users to post and share useful "snippets" of code.

fairlearn icon fairlearn

A Python package to assess and improve fairness of machine learning models.

gans-collection.torch icon gans-collection.torch

Torch implementation of various types of GAN (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN, LSGAN)

home-assistant icon home-assistant

:house_with_garden: Open source home automation that puts local control and privacy first

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