Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
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Updated
Sep 29, 2024 - Jupyter Notebook
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
A framework for integrated Artificial Intelligence & Artificial General Intelligence (AGI)
A curated awesome list of AI Startups in India & Machine Learning Interview Guide. Feel free to contribute!
Code for "Real-time self-adaptive deep stereo" - CVPR 2019 (ORAL)
螺旋熵减系统
Real-time GCC-NMF Blind Speech Separation and Enhancement
A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer (IJCAI 2019)
Codes and Project for Machine Learning Course, Fall 2018, University of Tabriz
This Repository contains Solutions to the Quizes & Lab Assignments of the Machine Learning Specialization (2022) from Deeplearning.AI on Coursera taught by Andrew Ng, Eddy Shyu, Aarti Bagul, Geoff Ladwig.
Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning
Stringlifier is on Opensource ML Library for detecting random strings in raw text. It can be used in sanitising logs, detecting accidentally exposed credentials and as a pre-processing step in unsupervised ML-based analysis of application text data.
螺旋熵减理论
PANDORA 💻
This code base is collection of codes that are freely available for google earth engine. This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy access to the normal users via web page.
✍️ An intelligent system that takes a document and classifies different writing styles within the document using stylometric techniques.
Building Recommendation Model for the electronics products of Amazon
pyMCR: Multivariate Curve Resolution for Python
Data Science + ML Cheat Sheet collection by me
The GOLang implementation of NeuroEvolution of Augmented Topologies (NEAT) method to evolve and train Artificial Neural Networks without error back propagation
PyTorch implementation of 'An Unsupervised Neural Attention Model for Aspect Extraction' by He et al. ACL2017'
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