tensorflow-data-pipelines
TensorFlow Data Pipelines
Build efficient, scalable data pipelines using the tf.data API for optimal training performance. This skill covers dataset creation, transformations, batching, shuffling, prefetching, and advanced optimization techniques to maximize GPU/TPU utilization.
Dataset Creation
From Tensor Slices
import tensorflow as tf
import numpy as np
# Create dataset from numpy arrays
x_train = np.random.rand(1000, 28, 28, 1)
y_train = np.random.randint(0, 10, 1000)
# Method 1: from_tensor_slices
dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
More from thebushidocollective/han
android-jetpack-compose
Use when building Android UIs with Jetpack Compose, managing state with remember/mutableStateOf, or implementing declarative UI patterns.
1.1Kfastapi-async-patterns
Use when FastAPI async patterns for building high-performance APIs. Use when handling concurrent requests and async operations.
785storybook-story-writing
Use when creating or modifying Storybook stories for components. Ensures stories follow CSF3 format, properly showcase component variations, and build successfully.
487atomic-design-fundamentals
Use when applying Atomic Design methodology to organize UI components into quarks, atoms, molecules, organisms, templates, and pages. Core principles and hierarchy.
364solid-principles
Use during implementation when designing modules, functions, and components requiring SOLID principles for maintainable, flexible architecture.
248angular-rxjs-patterns
Use when handling async operations in Angular applications with observables, operators, and subjects.
216