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PyTorch practice problems

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Title Difficulty
Adapter Layer Medium
Attention Block with RoPE Hard
Autoencoder Medium
Beam Search Hard
Binary Classifier Easy
BPE Encode Text Hard
Causal Attention Lab Medium
Causal LM Forward Pass Medium
Causal LM with KV Cache Generation Hard
Causal RoPE and SwiGLU Block Hard
Causal Self-Attention Block Medium
Cross-Attention Block Medium
Data Augmentation Medium
DDPM Sampler Loop Hard
DDPM Training Step End-to-End Medium
Decision Boundary Lab Medium
Denoising Autoencoder Lab Medium
Distributed Training Step End-to-End Hard
Encoder-Decoder Beam Search Hard
Encoder-Decoder Greedy Decode Medium
Encoder-Decoder Transformer Forward Pass Hard
Eval Loop with Metrics Medium
Feature Normalization Pipeline Easy
Gradient Accumulation Medium
Gradient Clipping Easy
GRU Sequence Forward Pass Medium
Image Pattern Lab Medium
Learning Rate Scheduler Medium
Linear Regression Easy
Logistic Regression From Scratch Easy
LSTM Sequence Forward Pass Medium
Mini-Batch Training Medium
MLM Evaluation: Masked Accuracy Medium
MLM Forward Pass Medium
MLM Forward with Tied Output Head Medium
MLM Masking Strategy Medium
Model Checkpointing Easy
Multi-Class Classifier Easy
Multi-Head Attention Block Hard
Nucleus Sampling Generation Loop Hard
One RNN Language-Model Training Step Hard
Patch Embedding with CLS Token Medium
Polynomial Regression Medium
RNN Sequence Forward Pass Medium
Self-Attention Layer Hard
Sequence Classifier Medium
Simple CNN Medium
Simple GAN Generator Hard
Skip Connection Block Medium
Tokenize and Pad Batch Medium