Mixed precision
Mixed precision is a computational technique that uses multiple numerical precisions for different values or operations to balance resource efficiency and numerical accuracy or stability.
Domain-Specific Small Language Models
Using reduced-precision numeric formats for some computations or weights to reduce memory use, often with limited loss in performance.
- How it is used
- Uses lower-precision arithmetic to reduce memory and improve computational efficiency during model training and inference.
- How deep it goes
- Explains FP16 and FP32 representation, training-time switching, gradient stability, and inference tradeoffs without detailing hardware kernels.
- Examples
- Uses GPT-Neo and Mistral 7B memory estimates, FP16 representation diagrams, and BF16 hardware-support checks.
- What you can do after
- Apply it by selecting lower-precision model formats to meet GPU memory constraints while preserving comparable performance.
Read how Domain-Specific Small Language Models explains mixed precision →
Deep Learning with R, Third Edition
Mixed-precision training is model training that uses numerical values with different floating-point precisions to balance computational efficiency and numerical accuracy.
- How it is used
- Speeds model training on GPUs or TPUs while preserving precision-sensitive weight updates.
- How deep it goes
- Explains dtype roles, unstable operations, loss scaling, and Keras configuration without deriving hardware-level implementation details.
- Examples
- Uses a mixed_float16 mini-GPT configuration, float32 layer opt-outs, and fixed or automatic optimizer loss scaling.
- What you can do after
- Operate/configure Keras mixed precision and protect training with float32 exceptions or loss scaling.
Read how Deep Learning with R, Third Edition explains mixed precision →
Deep Learning with Python, Third Edition
A training technique that uses lower-precision numeric types for some computations while keeping others in higher precision to balance speed, memory use, and numerical stability.
- How it is used
- Uses lower-precision computation to speed GPU or TPU training while preserving full precision for stability-sensitive updates.
- How deep it goes
- Explains layer dtypes, gradient casting, unstable operations, and loss scaling, but does not derive floating-point behavior mathematically.
- Examples
- Demonstrates Keras dtype policies, float32 opt-outs, fixed or automatic loss scaling, and a mini-GPT configuration.
- What you can do after
- Apply mixed precision in Keras, configure loss scaling, and protect numerically unstable layers with float32.
Read how Deep Learning with Python, Third Edition explains mixed precision →
Related concepts
- float16uses · Float16 is a 16-bit floating-point numeric format that represents values with less precision and storage than single-precision formats such as float32.
- FP32uses · FP32 is the IEEE 754 binary32 single-precision floating-point format, which represents approximate real numbers using 32 bits.
- PyTorchused by · PyTorch is an open-source machine-learning framework for Python that provides tensor computation, automatic differentiation, and tools for building and training neural networks.
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