annotated_deep_learning_paper_implementations
๐งโ๐ซ 60+ Implementations/tutorials of deep learning papers with side-by-side notes ๐; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), ๐ฎ reinforcement learning (ppo, dqn), capsnet, distillation, ... ๐ง
Learn to implement evidential deep learning for classification uncertainty. Transform neural network outputs into evidence parameters defining a Dirichlet distribution for explicit uncertainty quantification with labmlai.
Pre-LN and Post-LN Transformer Architectures: Key Differences and Implementation GuideUnderstand the key differences between Pre-LN and Post-LN transformer architectures. Learn how Pre-LN improves stable training of deep models and explore implementation guides.
How FNet Is Different From Standard Transformer Attention: A Deep Dive into Fourier-Based Token MixingDiscover how FNet revolutionizes Transformers by replacing attention with Fourier transforms. Experience linear complexity, reduced computation, and faster training. Learn the key differences today.
How Capsule Networks Implement Dynamic Routing in PyTorchLearn how Capsule Networks use dynamic routing via routing by agreement. Explore PyTorch implementation details and the Router class at labmlai annotated deep learning paper implementations.
Feedforward Transformer vs Standard Attention: Architectural Differences ExplainedDiscover the key differences between Feedforward Transformer and standard attention. Learn how Feedforward Transformers use FFNs solely, contrasting with attention's O(Lยฒ) complexity.
How Stable Diffusion Uses Latent Space for Image Generation: Technical Architecture ExplainedDiscover how Stable Diffusion leverages its latent space architecture for image generation. Learn about the text-conditioned U-Net and VAE encoding/decoding process.
How the Switch Transformer Routing Mechanism Works: Token-to-Expert Selection ExplainedLearn how the Switch Transformer routing mechanism assigns tokens to experts with softmax, enforces capacity limits, and scales output by confidence for efficient sparse training.
How ALiBi Removes Positional Embeddings While Preserving Attention PatternsDiscover how ALiBi removes positional embeddings by adding linear biases to attention logits. Learn how this preserves attention patterns & relative ordering without learned vectors.
RAdam vs Adam Optimizer: Key Differences for Stable Deep Learning TrainingDiscover the key differences between RAdam and Adam optimizers for stable deep learning training. RAdam offers automatic warm-up and faster convergence.
How the Dueling Network Architecture Is Implemented in DQNLearn how the dueling network architecture is implemented in DQN. Discover how state-value and action-advantage streams combine for stable learning in this detailed guide.
How PPO with GAE Improves Reinforcement Learning Sample EfficiencyDiscover how PPO with GAE enhances reinforcement learning sample efficiency. Learn to update policies stably and informatively with low-variance advantage estimates from limited data.
StyleGAN 2 vs Original GAN: 8 Key Architectural DifferencesDiscover 8 key architectural differences between StyleGAN 2 and original GANs. Learn how StyleGAN 2 achieves high-fidelity image synthesis with its improved generator-discriminator pipeline.
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