Recurrent Neural Networks (RNN) Basics

What is Recurrent Neural Network (RNN)?

• A type of artificial neural network that uses sequential data or time series data.
• Used for ordinal or temporal problems, such as language translation, natural language processing (NLP), speech recognition, and image captioning.
• Incorporated into popular applications such as Siri, voice search, and Google Translate.

How RNNs Work

• Pass sequential data to hidden layers one step at a time.
• Have a self-looping or recurrent workflow: hidden layer can remember and use previous inputs for future predictions in a short-term memory component.

Recurrent Neural Network Types

One-to-One

• Simplest type of RNN, allows a single input and a single output.
• Has fixed input and output sizes, acts as a traditional neural network.

One-to-Many

• Gives multiple outputs when given a single input.
• Takes a fixed input size and gives a sequence of data outputs.
• Applications include Music Generation and Image Captioning.

Many-to-One

• Used when a single output is required from multiple input units or a sequence of them.
• Takes a sequence of inputs to display a fixed output.

Many-to-Many

• Used to generate a sequence of output data from a sequence of input units.
• Divided into two subcategories: Equal Unit Size and Unequal Unit Size.

Equal Unit Size

• Number of both input and output units is the same.
• Application: Name-Entity Recognition.

Unequal Unit Size

• Inputs and outputs have different numbers of units.
• Application: Machine Translation.

Real-Life Example of RNN

• Apple's Siri and Google's voice search both use Recurrent Neural Networks (RNNs).

Information System (IS)

• A coordinated system of hardware, software, infrastructure, data, and people designed to generate, store, process, retrieve, and distribute information.

How IS can benefit from NLP, CNN, & RNN

Natural Language Processing (NLP)

• Helps IS understand and analyze text from emails, social media, or documents.
• Powers chatbots and virtual assistants to answer questions or help with tasks.
• Can summarize large texts automatically, saving time for users.

Convolutional Neural Networks (CNN)

• Help IS understand and analyze images, useful for tasks like identifying objects or scenes.
• Can read scanned documents or handwritten text, helping with tasks like digitizing documents.

Recurrent Neural Networks (RNN)

• Great for tasks involving sequences, like predicting future values in time series data or understanding speech.
• Can analyze user behavior over time to make personalized recommendations, like suggesting movies or products.

Learn the fundamentals of Recurrent Neural Networks (RNN), a type of artificial neural network used for sequential data and time series data. Understand its applications in language translation, NLP, speech recognition, and image captioning.