Assembled from the NeetCode ML course on NeetCode.io Built by Labibul Ahsan Alif on June 21, 2026
Every file in this project is code I wrote and submitted while completing the NeetCode ML course. The problems progressively build from gradient descent fundamentals all the way to a working GPT.
model/ Attention, Transformer, GPT architecture
attention.py Self-attention head
multi_head_attention.py Multi-headed attention
transformer.py Transformer block
gpt.py GPT model
normalization.py Layer normalization
batch_normalization.py Batch normalization
rms_normalization.py RMS normalization
embeddings.py Word embeddings
positional_encoding.py Positional encoding
kv_cache.py KV-Cache for fast inference
grouped_query_attention.py Grouped query attention
data/ Data pipeline
tokenizer.py BPE tokenizer
vocab.py Character-level vocabulary
loader.py Batched training data loader
dataset.py GPT dataset preparation
nlp_preprocessing.py NLP preprocessing
tokenizer_utils.py Tokenization edge cases
train.py GPT training loop
generate.py Text generation
foundations/ Neural network primitives built from scratch
neuron.py, backprop.py, mlp.py, activations.py, loss.py,
training_loop.py, dead_relu_detector.py, ...
pip install -r requirements.txt python train.py python generate.py
This project was built by completing the NeetCode ML Course:
- Math Foundations (gradient descent, activations, loss functions)
- Neural Networks from scratch (neuron, backprop, MLP)
- PyTorch fundamentals
- NLP pipeline (embeddings, tokenization, attention)
- Transformer architecture
- GPT model + text generation