Helpful TensorFlow Tutorials
September 9, 2026 · DeepAdvisors Team
The image-classification workflow we use on client projects: a tf.data pipeline that keeps the GPU fed, a KerasHub pretrained backbone, a two-stage fine-tune, honest evaluation, and export to TF Serving or LiteRT.
Read moreSeptember 9, 2026 · DeepAdvisors Team
Build the retrieval half of a production recommender: positives-only training data, two Keras towers with in-batch sampled softmax, logQ popularity correction, Recall@k evaluation, a ScaNN index, and a serving path that keeps index and model in sync.
Read moreSeptember 9, 2026 · DeepAdvisors Team
A practical TensorFlow forecasting playbook: build leak-free windows with tf.data, beat seasonal-naive baselines, compare linear, dilated CNN and attention models, add quantile intervals, and backtest with MASE before you ship.
Read moreSeptember 9, 2026 · DeepAdvisors Team
Build a production TFX pipeline end to end - Transform graphs that kill training/serving skew, a Keras Trainer, an Evaluator quality gate with sliced metrics and change thresholds, and the same code running locally and on Vertex AI Pipelines.
Read moreSeptember 9, 2026 · DeepAdvisors Team
Convert a Keras 3 model for the web, run it on the WebGPU backend with a WebGL fallback, avoid the tensor leaks and cold-start stalls that sink client-side inference, and verify parity with your server model.
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