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Welcome to my notes!
Algorithms
Algorithms
Game Theory
Intractability
Linear Programming
Markov Chains
Max Flow
Chess
Chess
Meta Learning Notes on Chess
Pawn Structure
Pawn Structure
The Caro-Slav Family
Pawns determine piece activity
Positional
Positional
Trading
Kingside pawn storm
The space tradeoff
Statics: a firm foundation
Weaknesses
Tactics Notes
Tactics Notes
Attractions
Calculation System Examples
Mating Patterns
Calculation system
ML
ML
ML Template
General Concepts
General Concepts
Gated Recurrent Unit
Multitask vs Multiclass vs Multilabel
GNN
GNN
Hierarchical GNN
Linear Algebra
Linear Algebra
Covariance
Loss Functions
Loss Functions
X Double Descent Loss Function
Math
Math
Mutual Information
Meta Notions
Meta Notions
O Local Minima in Deep Learning
O Neural Decision Trees
X Regularization
X Tips for training large models (OpenAI)
XO The Lottery Ticket Hypothesis
Self Supervised
Self Supervised
O CLIP
O Local and global features with SSL
O Supervised Contrastive Learning
X Class Aware Contrastive Learning
X InfoNCE Loss
XO Contrastive Learning for Unpaired Image to Image Translation
XO VICRegL learning local and global features
TCN
TCN
1x1 Convolution
Temporal Convolutional Networks
Transformers
Transformers
Attention in Recurrent Models
Positional Embeddings
Important resources
Resources
Vision Transformers
Vision Transformers
Vision Transformers
Temporal
Temporal
O Attention Based multi scale GRU for serial xrays
O Relating MHA to Convolution
O Sequential Models Deep Learning Textbook
O Standalone attention
O Temporal Graph Network
O Video Vision Transformer
X Gated Attention for Recency Bias
X Gated MultiHead Attention CNN
X Longitudinal SSL
XO TCN for disease prediction
XQ Which Transformer Architecture Fits my Data
machinelearning:: TCNs are wildin
Summary
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Ideas
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Abstract
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