Master advanced TensorFlow concepts for building, optimizing, and deploying complex machine learning models at scale. Covers performance tuning, distributed training, custom components, TFX pipelines, and model deployment strategies.
Prerequisites Intermediate TensorFlow & ML Concepts
Level:
Performance Optimization
Optimize TensorFlow models for speed and efficiency on GPUs/TPUs using profiling, mixed precision, and XLA.
Distributed Training
Implement various distributed training strategies (data/model parallelism) using tf.distribute.Strategy for large datasets and models.
Custom Components
Build custom layers, models, loss functions, metrics, and training loops using TensorFlow's advanced APIs.
Production Pipelines
Construct production-ready machine learning pipelines using TensorFlow Extended (TFX) components and orchestration.
Model Deployment
Deploy TensorFlow models for scalable inference using TensorFlow Serving and optimize models for edge devices with TensorFlow Lite.
Advanced Architectures
Implement components of sophisticated model architectures like Transformers and Generative Adversarial Networks (GANs).
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