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Reinforcement learning is the computational approach of learning from action by interacting with an environment through trial and error and receiving rewards (negative or positive) as feedback.
About Reinforcement Learning
https://www.youtube.com/watch?v=q0BiUn5LiBc
Gaming
Reinforcement learning is known for its application to video games. Since the games provide a safe environment for the agent to be trained in the sense that it is perfectly defined and controllable, this makes them perfect candidates for experimentation and will help a lot to learn about the capabilities and limitations of various RL algorithms.
but over iterations, the agent gets better and better with each episode of the training. This paper mainly investigates the performance of RL in popular games such as Minecraft or Dota2. https://huggingface.co/tasks/reinforcement-learning
About Reinforcement Learning
https://www.youtube.com/watch?v=q0BiUn5LiBc
Gaming
Reinforcement learning is known for its application to video games. Since the games provide a safe environment for the agent to be trained in the sense that it is perfectly defined and controllable, this makes them perfect candidates for experimentation and will help a lot to learn about the capabilities and limitations of various RL algorithms.
but over iterations, the agent gets better and better with each episode of the training. This paper mainly investigates the performance of RL in popular games such as Minecraft or Dota2. https://huggingface.co/tasks/reinforcement-learning
Visual Question Answering is the task of answering open-ended questions based on an image. They output natural language responses to natural language questions.
https://huggingface.co/tasks/visual-question-answering
About Visual Question Answering
Use Cases
Aid the Visually Impaired Persons
VQA models can be used to reduce visual barriers for visually impaired individuals by allowing them to get information about images from the web and the real world.
Education
VQA models can be used to improve experiences at museums by allowing observers to directly ask questions they interested in.
Improved Image Retrieval
Visual question answering models can be used to retrieve images with specific characteristics. For example, the user can ask "Is there a dog?" to find all images with dogs from a set of images.
Video Search
Specific snippets/timestamps of a video can be retrieved based on search queries. For example, the user can ask "At which part of the video does the guitar appear?" and get a specific timestamp range from the whole video.
https://huggingface.co/tasks/visual-question-answering
About Visual Question Answering
Use Cases
Aid the Visually Impaired Persons
VQA models can be used to reduce visual barriers for visually impaired individuals by allowing them to get information about images from the web and the real world.
Education
VQA models can be used to improve experiences at museums by allowing observers to directly ask questions they interested in.
Improved Image Retrieval
Visual question answering models can be used to retrieve images with specific characteristics. For example, the user can ask "Is there a dog?" to find all images with dogs from a set of images.
Video Search
Specific snippets/timestamps of a video can be retrieved based on search queries. For example, the user can ask "At which part of the video does the guitar appear?" and get a specific timestamp range from the whole video.
huggingface.co
What is Visual Question Answering? - Hugging Face
Learn about Visual Question Answering using Machine Learning
Video-Text-to-Text
No summary available for this task. Contribute by adding a nice description!
Placeholder page
This is a placeholder page for the Video-Text-to-Text task. It is incomplete and is open to community contributions.
How can I contribute ?
You can contribute to the various sections of this page, including datasets, metrics and models related to this task by opening a pull request to this repository.
https://huggingface.co/tasks/video-text-to-text
No summary available for this task. Contribute by adding a nice description!
Placeholder page
This is a placeholder page for the Video-Text-to-Text task. It is incomplete and is open to community contributions.
How can I contribute ?
You can contribute to the various sections of this page, including datasets, metrics and models related to this task by opening a pull request to this repository.
https://huggingface.co/tasks/video-text-to-text
Document Question Answering (also known as Document Visual Question Answering) is the task of answering questions on document images. Document question answering models take a (document, question) pair as input and return an answer in natural language. Models usually rely on multi-modal features, combining text, position of words (bounding-boxes) and image.
About Document Question Answering
Use Cases
Document Question Answering models can be used to answer natural language questions about documents. Typically, document QA models consider textual, layout and potentially visual information. This is useful when the question requires some understanding of the visual aspects of the document. Nevertheless, certain document QA models can work without document images. Hence the task is not limited to visually-rich documents and allows users to ask questions based on spreadsheets, text PDFs, etc!
https://huggingface.co/tasks/document-question-answering
About Document Question Answering
Use Cases
Document Question Answering models can be used to answer natural language questions about documents. Typically, document QA models consider textual, layout and potentially visual information. This is useful when the question requires some understanding of the visual aspects of the document. Nevertheless, certain document QA models can work without document images. Hence the task is not limited to visually-rich documents and allows users to ask questions based on spreadsheets, text PDFs, etc!
https://huggingface.co/tasks/document-question-answering
Tabular regression is the task of predicting a numerical value given a set of attributes.
About Tabular Regression
About the Task
Tabular regression is the task of predicting a numerical value given a set of attributes/features. Tabular meaning that data is stored in a table (like an excel sheet), and each sample is contained in its own row. The features used to predict our target can be both numerical and categorical. However, including categorical features often requires additional preprocessing/feature engineering (a few models do accept categorical features directly, like CatBoost). An example of tabular regression would be predicting the weight of a fish given its' species and length.https://huggingface.co/tasks/tabular-regression
About Tabular Regression
About the Task
Tabular regression is the task of predicting a numerical value given a set of attributes/features. Tabular meaning that data is stored in a table (like an excel sheet), and each sample is contained in its own row. The features used to predict our target can be both numerical and categorical. However, including categorical features often requires additional preprocessing/feature engineering (a few models do accept categorical features directly, like CatBoost). An example of tabular regression would be predicting the weight of a fish given its' species and length.https://huggingface.co/tasks/tabular-regression
Text-to-Speech
Text-to-Speech (TTS) is the task of generating natural sounding speech given text input. TTS models can be extended to have a single model that generates speech for multiple speakers and multiple languages.
About Text-to-Speech
https://youtu.be/NW62DpzJ274
Use Cases
Text-to-Speech (TTS) models can be used in any speech-enabled application that requires converting text to speech imitating human voice.
Text-to-Speech (TTS) is the task of generating natural sounding speech given text input. TTS models can be extended to have a single model that generates speech for multiple speakers and multiple languages.
About Text-to-Speech
https://youtu.be/NW62DpzJ274
Use Cases
Text-to-Speech (TTS) models can be used in any speech-enabled application that requires converting text to speech imitating human voice.
Automatic Speech Recognition
Automatic Speech Recognition (ASR), also known as Speech to Text (STT), is the task of transcribing a given audio to text. It has many applications, such as voice user interfaces.
About Automatic Speech Recognition
https://youtu.be/TksaY_FDgnk
Use Cases
Virtual Speech Assistants
Many edge devices have an embedded virtual assistant to interact with the end users better. These assistances rely on ASR models to recognize different voice commands to perform various tasks. For instance, you can ask your phone for dialing a phone number, ask a general question, or schedule a meeting.
https://huggingface.co/tasks/automatic-speech-recognition
Automatic Speech Recognition (ASR), also known as Speech to Text (STT), is the task of transcribing a given audio to text. It has many applications, such as voice user interfaces.
About Automatic Speech Recognition
https://youtu.be/TksaY_FDgnk
Use Cases
Virtual Speech Assistants
Many edge devices have an embedded virtual assistant to interact with the end users better. These assistances rely on ASR models to recognize different voice commands to perform various tasks. For instance, you can ask your phone for dialing a phone number, ask a general question, or schedule a meeting.
https://huggingface.co/tasks/automatic-speech-recognition
Audio-to-Audio
Audio-to-Audio is a family of tasks in which the input is an audio and the output is one or multiple generated audios. Some example tasks are speech enhancement and source separation.
Use Cases
Speech Enhancement (Noise removal)
Speech Enhancement is a bit self explanatory. It improves (or enhances) the quality of an audio by removing noise. There are multiple libraries to solve this task, such as Speechbrain, Asteroid and ESPNet. Here is a simple example using Speechbrain
https://huggingface.co/tasks/audio-to-audio
Audio-to-Audio is a family of tasks in which the input is an audio and the output is one or multiple generated audios. Some example tasks are speech enhancement and source separation.
Use Cases
Speech Enhancement (Noise removal)
Speech Enhancement is a bit self explanatory. It improves (or enhances) the quality of an audio by removing noise. There are multiple libraries to solve this task, such as Speechbrain, Asteroid and ESPNet. Here is a simple example using Speechbrain
https://huggingface.co/tasks/audio-to-audio
Audio Classification
Audio classification is the task of assigning a label or class to a given audio. It can be used for recognizing which command a user is giving or the emotion of a statement, as well as identifying a speaker.
About Audio Classification
Use Cases
Command Recognition
Command recognition or keyword spotting classifies utterances into a predefined set of commands. This is often done on-device for fast response time.
Audio classification is the task of assigning a label or class to a given audio. It can be used for recognizing which command a user is giving or the emotion of a statement, as well as identifying a speaker.
About Audio Classification
Use Cases
Command Recognition
Command recognition or keyword spotting classifies utterances into a predefined set of commands. This is often done on-device for fast response time.
Tabular Regression
Tabular regression is the task of predicting a numerical value given a set of attributes.
About Tabular Regression
About the Task
Tabular regression is the task of predicting a numerical value given a set of attributes/features. Tabular meaning that data is stored in a table (like an excel sheet), and each sample is contained in its own row. The features used to predict our target can be both numerical and categorical. However, including categorical features often requires additional preprocessing/feature engineering (a few models do accept categorical features directly, like CatBoost). An example of tabular regression would be predicting the weight of a fish given its' species and length.
Use Cases
Sales Prediction: a Use Case for Predicting a Continuous Target Variable
Here the objective is to predict a continuous variable based on a set of input variable(s).
https://huggingface.co/tasks/tabular-regression
Tabular regression is the task of predicting a numerical value given a set of attributes.
About Tabular Regression
About the Task
Tabular regression is the task of predicting a numerical value given a set of attributes/features. Tabular meaning that data is stored in a table (like an excel sheet), and each sample is contained in its own row. The features used to predict our target can be both numerical and categorical. However, including categorical features often requires additional preprocessing/feature engineering (a few models do accept categorical features directly, like CatBoost). An example of tabular regression would be predicting the weight of a fish given its' species and length.
Use Cases
Sales Prediction: a Use Case for Predicting a Continuous Target Variable
Here the objective is to predict a continuous variable based on a set of input variable(s).
https://huggingface.co/tasks/tabular-regression
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