Name: Arunkumar Venkataramanan
Type: User
Company: AI Founder @Deep-Brainz & Stealth AI Labs
Bio: Research Scientist | GenAI & AGI | Serial Entrepreneur, AI Innovator | Founder, CEO, Chief Product & Technologist at @Deep-Brainz AI, GenAI/AGI Stealth Startups
Twitter: arunkumar_bvr
Location: Bangalore, India
Blog: https://arunkumarramanan.github.io
Arunkumar Venkataramanan's Projects
📢 Ready to learn!✅ you will learn 10 skills as data scientist:📚 Machine Learning, Deep Learning, Data Cleaning, EDA, Learn Python, Learn python packages such as Numpy, Pandas, Seaborn, Matplotlib, Plotly, Tensorfolw, Theano...., Linear Algebra, Big Data, Analysis Tools and solve some real problems such as predict house prices.🔖
Fork this template for the 100 days journal - to keep yourself accountable
100 Days of ML Coding
100 Must-Read NLP Papers
These are the instructions for "100 Days of ML Code" By Siraj Raval on Youtube
Public material for CS109
2018 Data Science Bowl 2nd Place Solution
For the next 30 days, learn the Python Programming language.
Curated collection of useful JavaScript snippets that you can understand in 30 seconds or less.
A curated collection of common interview questions to help you prepare for your next interview.
📜 33 concepts every JavaScript developer should know.
Codes and dashboards for 4th place solution for Kaggle's Home Credit Default Risk competition
My solution rank 5th/1212 in Facebook check ins prediction competition at Kaggle
Learn Deep Reinforcement Learning in Depth in 60 days
Curated list of resources for college students :octocat: Show your :heart: by giving a :star:
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Fast and flexible AutoML with learning guarantees.
Second Annual Data Science Bowl
🎮 Advanced Deep Learning and Reinforcement Learning at UCL & DeepMind | YouTube videos 👉
Code and hyperparameters for the paper "Generative Adversarial Networks"
This is a library dedicated to adversarial machine learning. Its purpose is to allow rapid crafting and analysis of attacks and defense methods for machine learning models. The Adversarial Robustness Toolbox provides an implementation for many state-of-the-art methods for attacking and defending classifiers. https://developer.ibm.com/code/open/projects/adversarial-robustness-toolbox/
The classical papers and codes about generative adversarial nets
A machine learning package built for humans.
Efficient Batched Reinforcement Learning in TensorFlow
A full-featured, hackable Next.js AI chatbot built by Vercel Labs
All-in-one AI container for rapid prototyping
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Experience, Learn and Code the latest breakthrough innovations with Microsoft AI
Automatically exported from code.google.com/p/aima-python
Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"