Video
Video is time-ordered visual data represented as a sequence of image frames, often accompanied by synchronized audio.
AI-Powered Search
Video is time-ordered visual data consisting of multiple images, optionally accompanied by synchronized audio.
- How it is used
- Positions video as unstructured data and as a modality for multimodal search.
- How deep it goes
- Defines video structurally but does not detail indexing, representation, or retrieval strategies for it.
- Examples
- Uses a comparative figure showing video alongside text, images, and audio as unstructured-data types.
- What you can do after
- Evaluate video as a time-based combination of visual frames and optional synchronized audio in search systems.
Data Analysis with LLMs
Video data consists of sequences of visual frames, typically accompanied by motion and possibly audio.
- How it is used
- Supports extracting useful insights from video, especially by generating titles from selected visual frames.
- How deep it goes
- Explains frame-based multimodal prompting and cost implications, but stops short of implementation details for extracting frames.
- Examples
- Uses selected frames showing cars and possibly a location to demonstrate automatic title generation.
- What you can do after
- Build video-processing applications that submit frames and instructions to generate suitable titles.
Deep Learning with Python, Third Edition
Data in which each example is a sequence of image frames, typically stored as a rank-5 tensor for a batch.
- How it is used
- Represents batches of videos as ordered sequences of image data for later neural-network manipulation.
- How deep it goes
- Defines the rank-5 shape and axis meanings, but stops short of explaining video-processing operations or model architectures.
- Examples
- Uses a generic batch of video clips organized by samples, frames, height, width, and channels.
- What you can do after
- Evaluate whether video data has been arranged with the expected five tensor axes.
Read how Deep Learning with Python, Third Edition explains video →
Deep Learning with R, Third Edition
Video data represents videos as ordered sequences of image frames.
- How it is used
- Represents datasets in which each sample is a sequence of image frames.
- How deep it goes
- Defines the rank-5 shape and names its axes without discussing video models or processing operations.
- Examples
- Uses the symbolic shape (samples, frames, height, width, channels) to show how a batch of videos is organized.
- What you can do after
- Evaluate video datasets as rank-5 tensors by identifying samples, frames, spatial dimensions, and channels.
Read how Deep Learning with R, Third Edition explains video →
WordPress Power Toolkit: Harness AI to build next-level websites
Video is time-based visual content that may include synchronized sound and is delivered digitally.
- How it is used
- Uses video to make websites more engaging while requiring purposeful selection and visitor-controlled playback.
- How deep it goes
- Explains video storage, bandwidth, compression, streaming, embedding, and a step-by-step YouTube upload workflow.
- Examples
- Uses a five-minute video’s storage comparison and a detailed YouTube upload sequence as concrete examples.
- What you can do after
- Operate YouTube to upload, configure, publish, schedule, and embed video on a website.
Read how WordPress Power Toolkit: Harness AI to build next-level websites explains video →
Related concepts
- multimodalused by · A multimodal model is a model that processes, generates, or relates information represented in two or more data modalities, such as text, images, audio, or video.
- rank-5 tensoruses · A rank-5 tensor is an array with five axes or dimensions.
- technical articleused by · A written article that explains or discusses a technical subject.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.