Book · Machine Learning with PyTorch and Scikit-Learn

Sequence Modeling with PyTorch

A PyTorch sequence-modeling milestone covering recurrent hidden states, LSTM sentiment classification, and character-level language modeling.

nn.RNN internalsEmbeddingsLSTMIMDB sentimentCharacter language model

GitHub

What this project is about

This book milestone collects the PyTorch sequence path: RNN basics, hidden-state mechanics, LSTM sentiment analysis, and character-level language modeling.

Implementation focus

It helped me understand recurrent networks from inside PyTorch: tensors, modules, embeddings, hidden states, packed sequences ideas, losses, and training loops.

Key results and artifacts

IMDB vocabulary: ~69,023 tokens
Character corpus: 1,112,350 characters
Language-model vocabulary: 80 characters
LSTM language-model loss dropped from ~4.37 to low 1.x range

What I learned

This was the PyTorch bridge from static examples into dynamic sequences, memory, and generative text.

Milestone deep dive

What this project proves in my learning path.

A PyTorch sequence-modeling milestone covering recurrent hidden states, LSTM sentiment classification, and character-level language modeling. The milestone is included because it shows a complete learning unit: a problem, a data representation, an implementation path, measurable outputs, and a lesson that later connects to larger AI systems.

Input layerordered sequences of text, audio, dates, or musical events

Model layerrecurrent or attention-based sequence model

Evaluation layertests, metrics, plots, or qualitative output

Artifact layerclassification, generation, alignment, or trigger probabilities

Problem framing

What I was trying to solve

This page is not just a lab description. It explains how I transformed the assignment idea into an engineering story: what the model receives, what it optimizes, what output it should produce, and how the result can be inspected.

Implementation

What I coded or assembled

The implementation focus was the mechanism behind the result: functions, model blocks, training logic, preprocessing, evaluation, and the small technical details that make the notebook work rather than only look correct.

Concepts

Core concepts

nn.RNN internalsEmbeddingsLSTMIMDB sentimentCharacter language model

These tags show the technical ideas this milestone added to my toolkit.

Learning result

What changed after it

This was the PyTorch bridge from static examples into dynamic sequences, memory, and generative text.

Important outputs and evidence

IMDB vocabulary: ~69,023 tokens
Character corpus: 1,112,350 characters
Language-model vocabulary: 80 characters
LSTM language-model loss dropped from ~4.37 to low 1.x range

How I would explain it in an interview

I would describe this milestone by starting from the data representation, then explain the model or algorithm, then show what was measured. The important part is not only the final number; it is the ability to reason about why the method works, what assumptions it makes, and what limitations should be improved next.