All exercises for the course Elements of AI - Building AI
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Updated
Jan 13, 2022 - Python
All exercises for the course Elements of AI - Building AI
[TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training
PyTorch code for FLD (Feature Likelihood Divergence), FID, KID, Precision, Recall, etc. using DINOv2, InceptionV3, CLIP, etc.
Decision Tree classifier from scratch without any machine learning libraries
[ICLR 2021] "Robust Overfitting may be mitigated by properly learned smoothening" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chang, Zhangyang Wang
JAX implementation of deep RL agents with resets from the paper "The Primacy Bias in Deep Reinforcement Learning"
Pytorch implementation of the paper: "BMN: Boundary-Matching Network for Temporal Action Proposal Generation", along with three new modules to address overfitting issues found in the baseline model, and their ablation studies.
Official Codebase of "A Closer Look at Weakly-Supervised Audio-Visual Source Localization" (NeurIPS 2022)
42AI Bootcamps - Python & Machine Learning
Elements of AI: Building AI - Advanced is an online course by Reaktor and University of Helsinki worth 2 ECTS.
ID3 decision tree implementation
Dropout: A Simple Way to Prevent Neural Networks from Overfitting
competitions launched on website AnalyticsVidhya.com
Model search in traditional machine learning algorithms (non DL) and DL starter codes on MNIST dataset. This is a good starter code for beginners trying to learn about curse of dimensionality, overfitting and other concepts in general
Supplementary code for the paper "Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks" - an accepted paper of LOD2019
Udacity Self Driving Car Nanodegree - Behavioral Cloning
spatial resampling for more robust cross validation in spatial studies
Clipped Noise Softmax to overcome over-fitting with Softmax - PyTorch implementation
DisturbValue: a regularization method for regressioin tasks
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