Skip to content

Sequential & NLP Models

Understand the flow of data over time. Learn to build systems that process information in order—whether you're forecasting the stock market or creating a chatbot that remembers the start of a conversation.

Motivation

RNNs and LSTMs

Give your models a memory

Standard AI looks at data points in isolation. Recurrent Neural Networks (RNNs) use loops to remember what happened just a moment ago, making them ideal for text and sensor data.

What you'll learn:

  • Hidden States: How models pass "memories" from one step to the next.
  • The Vanishing Gradient: Why basic models struggle with long sentences.
  • LSTMs & GRUs: Specialized tools that learn to "forget" useless info and focus on what matters.

Time Series Forecasting

Predict the future using the past

Move beyond simple guesses. Use deep learning to analyze patterns in time-stamped data, like energy demand or market shifts, to predict what comes next.

What you'll learn:

  • Windowing: How to slice long histories into bite-sized training pieces.
  • Seasonality: Identifying repeating cycles (like holiday shopping surges).
  • Multi-step Prediction: Forecasting an entire week ahead instead of just tomorrow.

Understanding NLP

See the big picture before writing a single line of code

Before building NLP systems, you need to understand how they think. Learn how raw human language travels through a pipeline and gets transformed into something a machine can reason about.

What you'll learn:

  • The NLP Pipeline: The full journey from raw text to model output.
  • Encodings vs. Embeddings: Why one is a lookup table and the other carries meaning.
  • The Semantic Gap: How machines learn that "King - Man + Woman = Queen."

Word Embeddings

Turn language into math

Computers don't read words; they read numbers. Learn how tools like Word2Vec turn words into coordinates in a digital map so the computer understands that "King" and "Queen" are related.

What you'll learn:

  • Dense Embeddings: Why points in a mathematical space represent meaning better than simple lists.
  • Semantic Similarity: Calculating how "close" two words are in meaning.
  • Word Relationships: The logic that allows a model to understand that "King - Man + Woman = Queen."

Evaluation Metrics for NLP

Measure if your model actually "gets it"

You can't just use "Percent Correct" for language. If a model translates a sentence perfectly but uses a different synonym, is it wrong? Learn the industry standards for grading AI text.

What you'll learn:

  • BLEU Score: The standard for checking translation quality.
  • ROUGE: How to measure if a summary captured the main points.
  • Perplexity: A metric that calculates how "confused" a model is by new text.

Question Answering Bots

Find the needle in the haystack

Combine memory and word meanings to build bots that can read a 50-page manual and instantly find the specific answer a user is looking for.

What you'll learn:

  • Context Windows: Helping the AI stay focused on the relevant text.
  • Span Prediction: Teaching the model to highlight the exact start and end of an answer.
  • Search & Extract: The logic behind modern AI assistants and document search tools.

Language is a journey, not a snapshot. By understanding memory and word relationships, you transform raw text into the intelligence that powers the modern world.