labml-nn 0.4.137

🧑‍🏫 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit), optimizers (adam, radam, adabelief), gans(dcgan, cyclegan, stylegan2), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, diffusion, etc. 🧠

labml.ai Deep Learning Paper Implementations

This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations,

The website renders these as side-by-side formatted notes. We believe these would help you understand these algorithms better.

We are actively maintaining this repo and adding new implementations almost weekly. for updates.

Paper Implementations

✨ Transformers

• Multi-headed attention
• Transformer building blocks
• Transformer XL
  • Relative multi-headed attention
• Rotary Positional Embeddings
• Attention with Linear Biases (ALiBi)
• RETRO
• Compressive Transformer
• GPT Architecture
• GLU Variants
• kNN-LM: Generalization through Memorization
• Feedback Transformer
• Switch Transformer
• Fast Weights Transformer
• FNet
• Attention Free Transformer
• Masked Language Model
• MLP-Mixer: An all-MLP Architecture for Vision
• Pay Attention to MLPs (gMLP)
• Vision Transformer (ViT)
• Primer EZ
• Hourglass

✨ Low-Rank Adaptation (LoRA)

✨ Eleuther GPT-NeoX

• Generate on a 48GB GPU
• Finetune on two 48GB GPUs
• LLM.int8()

✨ Diffusion models

• Denoising Diffusion Probabilistic Models (DDPM)
• Denoising Diffusion Implicit Models (DDIM)
• Latent Diffusion Models
• Stable Diffusion

✨ Generative Adversarial Networks

• Original GAN
• GAN with deep convolutional network
• Cycle GAN
• Wasserstein GAN
• Wasserstein GAN with Gradient Penalty
• StyleGAN 2

✨ Recurrent Highway Networks

✨ LSTM

✨ HyperNetworks - HyperLSTM

✨ ResNet

✨ ConvMixer

✨ Capsule Networks

✨ U-Net

✨ Sketch RNN

✨ Graph Neural Networks

• Graph Attention Networks (GAT)
• Graph Attention Networks v2 (GATv2)

✨ Counterfactual Regret Minimization (CFR)

Solving games with incomplete information such as poker with CFR.

• Kuhn Poker

✨ Reinforcement Learning

• Proximal Policy Optimization with Generalized Advantage Estimation
• Deep Q Networks with with Dueling Network, Prioritized Replay and Double Q Network.

✨ Optimizers

• Adam
• AMSGrad
• Adam Optimizer with warmup
• Noam Optimizer
• Rectified Adam Optimizer
• AdaBelief Optimizer
• Sophia-G Optimizer

✨ Normalization Layers

• Batch Normalization
• Layer Normalization
• Instance Normalization
• Group Normalization
• Weight Standardization
• Batch-Channel Normalization
• DeepNorm

✨ Distillation

✨ Adaptive Computation

• PonderNet

✨ Uncertainty

• Evidential Deep Learning to Quantify Classification Uncertainty

✨ Activations

• Fuzzy Tiling Activations

✨ Langauge Model Sampling Techniques

• Greedy Sampling
• Temperature Sampling
• Top-k Sampling
• Nucleus Sampling

✨ Scalable Training/Inference

• Zero3 memory optimizations

Installation

pip install labml-nn