How do I learn to implement ML papers from scratch?

Start by choosing a paper that matches your interest: Transformers for NLP, ResNet for computer vision, or GANs for generative models. Break the paper into components and implement each as a coding problem. Test your implementation against known results. Build progressively: start with RNN, then LSTM, then Transformers.

Which ML paper should I implement first?

For computer vision beginners: VGG (simple) → ResNet (residual connections). For NLP: RNN → LSTM → Transformers. For generative models: VAE → GANs → Diffusion Models. Each builds foundational concepts for more complex architectures.

What is the difference between implementing Transformers and BERT?

Transformers (Attention Is All You Need) is the base encoder-decoder architecture. BERT uses only the transformer encoder with: (1) Bidirectional attention (sees full context), (2) Masked Language Modeling for pre-training, (3) Next Sentence Prediction task, (4) WordPiece tokenization. BERT is a transformer-based model with specific pre-training objectives.

How do I implement ResNet from scratch in PyTorch?

ResNet implementation steps: (1) Create BasicBlock with two 3x3 convs and skip connection, (2) Create Bottleneck block with 1x1→3x3→1x1 convs for deeper networks, (3) Handle dimension mismatch with 1x1 projection in skip connection, (4) Stack blocks: conv1 (7x7) → layer1 (64 channels) → layer2 (128) → layer3 (256) → layer4 (512) → global avg pool → FC layer.

What are the key components to implement GANs?

GAN implementation requires: (1) Generator: maps noise vector to fake image using transposed convolutions, (2) Discriminator: binary classifier (real vs fake) using convolutions, (3) Adversarial loss: minimax game where generator minimizes log(1-D(G(z))) and discriminator maximizes log(D(x)) + log(1-D(G(z))), (4) Alternating training: train D on real+fake batch, then train G to fool D, (5) Techniques to prevent mode collapse.

How does U-Net differ from regular CNNs?

U-Net is an encoder-decoder with skip connections for pixel-wise segmentation: (1) Contracting path (encoder): conv blocks with max pooling to capture context, (2) Expanding path (decoder): upsampling with transposed convs to enable precise localization, (3) Skip connections: concatenate encoder features to decoder at matching resolutions, preserving spatial information lost during downsampling. The U-shape gives it its name.

Research Papers

Attention Is All You Need

Tokenization

Convert text to token IDs

Embedding Layer

Map tokens to vectors

Positional Encoding

Inject position via sinusoids

Scaled Dot-Product Attention

Core QKV mechanism

Multi-Head Attention

Parallel attention heads

Feed-Forward Network

Position-wise MLP

Layer Normalization

Normalize activations

Encoder Block

Complete encoder layer

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