Introduction
ML Fundamentals
Classical Machine Learning
- Basics
- Unsupervised Learning
Neural Networks
Loss
Computer Vision
NLP
Parallel Computing
Anomaly Detection
- DBSCAN
- Autoencoder
Visualization

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Pooling
Model compression
Weight Pruning
Two (Multi Task Learning) MTL methods for Deep Learning
Hard parameter sharing
Soft parameter sharing
Deep Learning
CNN
2d Convolution
Bottleneck layer
Reference

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Neural Networks

Basics

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