– What does ‘sequence data’ mean? Discuss the different types
– Compare the different Sequence models (RNN, LSTM, GRU, and Transformers)
– Briefly describe the architecture of a Recurrent Neural Network (RNN)
– What is Long-Short Term Memory (LSTM)?
Sequence models are a class of machine learning models designed for tasks that involve sequential data, where the order of elements in the input is important. Sequential data includes textual data, time series data, audio signals, video streams or any other ordered data. These sequences can be of varying lengths and the elements of the sequence are dependent upon each other. Unlike traditional machine learning algorithms, sequence models are specifically built to process data that is not independently and identically distributed (i.i.d.) but instead carries some dependency with each other.
Key Sequence models
- Recurrent Neural Networks (RNNs): RNNs are a fundamental type of sequence model. They process sequences one element at a time while maintaining an internal hidden state that stores information about previous elements in the sequence. This allows them to capture dependencies across time steps. However, traditional RNNs suffer from the “vanishing gradient” problem, which limits their ability to capture long-range dependencies.
- Long Short-Term Memory (LSTM) Networks: LSTMs are a type of RNN designed to overcome the vanishing gradient problem. They introduce specialized memory cells and gating mechanisms that allow them to capture and preserve information over long sequences.
- Gated Recurrent Units (GRUs): GRUs are another variant of RNNs that are similar to LSTMs but with a simplified structure. They also use gating mechanisms to control the flow of information within the network. They are computationally more efficient than LSTMs while still being able to capture dependencies in sequential data.
- Transformer Models: Transformers are a more recent and highly effective architecture for sequence modeling. They rely on a self-attention mechanism to process sequences in parallel and capture long-term dependencies in data, making them more efficient than traditional RNNs. Transformers have been particularly successful in NLP tasks and have led to models like BERT, GPT, and others.
Applications of Sequence models
|S.No.||Application||Sequence model used in:||Examples|
|1||Natural Language Processing||- Search / Question Answering|
- Machine Translation
- Sentiment Analysis
- Text Classification
- Text Generation
|- Bing Search|
- Google Translate
- Eno by CapOne
- Sentiment in social media posts
- Spam/No spam in Gmail
|2||Speech Recognition||Speech-to-Text Conversion||- Alexa, Siri, Google Assistant|
|3||Time Series Analysis||- Stock Price Prediction|
- Weather Forecasting
|- Bloomberg Finance|
- IBM Weather app
|4||Healthcare||- Medical Devices|
- Drug Discovery
|- Medtronic’s Real-Time AI Endoscopy Device|
- Evozyne used NVIDIA BioNeMo for AI protein identification to engineer new proteins
|5||Video Analysis||- Action Recognition|
- Video Captioning
|- Surveillance cameras|
- Amazon Prime Video to identify the actors in the frame
|6||Music Generation||Music Composition||- Magenta's AI Composer uses sequence models to create music|
|7||Autonomous Driving||Behavior Prediction of vehicles, pedestrians, and obstacles||- Tesla for the autonomous driving feature|
|8||Genomics||DNA Sequence Analysis||PacBio's Revio is a long-read sequencing system designed to sequence human genomes|
|9||Fraud Detection||Credit Card Fraud Detection||- American Express uses LSTM to detect anomalous patterns in transactions|
The table above only scratches the surface of the myriad real-world applications for Sequence models. These models are proving highly effective across a wide range of industries, and they are poised to revolutionize the way business is conducted in numerous sectors.
- In the “Sequence Model Complete Course” lecture video, Prof. Andrew Ng explains the concept of Sequence data and Sequence models using multiple examples (Runtime: First 12 mins). In the rest of the video, he goes deeper into each type of Sequence model (RNN, LSTM, GRU and Transformers) and explain the concepts in detail (Total Runtime: 5hr 55 mins)
- The playlist also consists of lecture videos by Prof. Chris Manning from the Stanford NLP course. In the first video titled “Recurrent Neural Networks”, Prof. Manning introduces Neural Dependency Parsing and Language models, which serves as a great build up for the introduction of Sequence Models (Runtime: 1 hr 19 mins)
- The second video titled “Simple and LSTM RNNs” goes into the topics of RNN, LSTM in detail (Runtime: 1 hr 21 mins)