Deep learning framework
A deep learning framework is software that provides abstractions and computational tools for building, training, and running neural-network models.
Natural Language Processing in Action, Second Edition
A deep learning framework is software that provides abstractions and computational support for constructing, training, and running neural-network models.
- How it is used
- Supports constructing, training, and running neural-network models while automating gradient computation and weight adjustment.
- How deep it goes
- Explains backpropagation’s gradient-and-weight process, then compares framework APIs, GPU support, deployment suitability, and ecosystem direction.
- Examples
- Uses a Wikipedia software-comparison table, pandas transformations, capability scoring, and a portability ranking led by PyTorch.
- What you can do after
- Apply it via PyTorch or TensorFlow to construct and train neural-network models, choosing APIs and deployment support deliberately.
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Deep Learning with R, Third Edition
A deep learning framework is a software system that provides reusable abstractions and computational capabilities for building and training neural networks.
- How it is used
- Replaces low-level implementation work when building and training neural networks.
- How deep it goes
- Defines three core capabilities and sketches their historical emergence, but does not compare framework internals.
- Examples
- Names Keras, JAX, TensorFlow, and PyTorch as major frameworks, contrasting them with hand-written C++ gradient logic.
- What you can do after
- Evaluate Keras, JAX, TensorFlow, and PyTorch as reusable alternatives to low-level neural-network implementations.
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Deep Learning with Python, Third Edition
Software libraries for building and training neural network models.
- How it is used
- Replaces low-level neural-network implementation with reusable tooling for model construction, gradient computation, hardware execution, and distribution.
- How deep it goes
- Defines three core capabilities and gives historical context, but does not detail framework-specific APIs or implementation internals.
- Examples
- Contrasts hand-written C++ gradient logic and one-off CUDA implementations with reusable multi-device frameworks.
- What you can do after
- Builds neural-network systems without implementing gradient logic, hardware execution, and distributed computation from scratch.
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Related concepts
- PyTorchincludes · PyTorch is an open-source machine-learning framework for Python that provides tensor computation, automatic differentiation, and tools for building and training neural networks.
- Kerasincludes · Keras is an open-source, high-level deep-learning API for building, training, evaluating, and deploying neural-network models.
- JAXincludes · JAX is a Python library for high-performance array-based numerical computing with composable transformations including automatic differentiation, compilation, vectorization, and parallelization.
- TensorFlowincludes · TensorFlow is an open-source software platform for numerical computation and machine learning, with tools for developing, training, and deploying models.
- automatic differentiationuses · Automatic differentiation is a computational technique that evaluates derivatives of a program by systematically applying the chain rule to its elementary operations.
- Model Servingaddresses · A model-serving platform is software infrastructure that hosts trained models and exposes inference by accepting requests and returning model outputs.
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