Writing about AI, machine learning, and full‑stack engineering with a focus on clarity and implementation.
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This guide covers everything from setting up Android Studio and real-device debugging, to implementing a modern camera-based UI, integrating on-device ML inference, and deploying your model for fast, offline predictions.
We build, train, and fix a deep neural network that learns to tell sunflowers from daisies. Then we ship it as a .tflite file, ready for your phone.
Learn how to build image-generating AI models from the ground up. This hands-on tutorial walks you through the mathematics, architecture, and implementation of generative models including VAEs and GANs, with code you can run yourself.
In this first part, we'll dive deep into custom image preprocessing pipelines, dataset preparation with TensorFlow, and performance optimization techniques to get your data ready for model training.