Advanced Topics in TensorFlow

🧠 Custom Training Loops

• Key Concepts: Use tf.GradientTape for automatic differentiation, implement custom training logic, and optimize with tf.keras.optimizers.
• 🔗 Official Guide on Custom Training

🌐 Distributed Training

• Scalability: Leverage tf.distribute.Strategy for multi-GPU, TPU, or multi-node training.
• Best Practices: Use tf.data.Dataset for efficient data sharding and tf.keras.Model.fit with distributed_strategy argument.
• 🔗 Distributed Training Documentation

🛠️ Custom Layers & Models

• Flexibility: Build custom layers by extending tf.keras.layers.Layer, and models via tf.keras.Model.
• Examples: Implement custom activation functions, loss layers, or model architectures.
• 🔗 Custom Layers Tutorial

📚 Advanced Topics Resources

• 📖 TensorFlow Core Concepts
• 🧠 Advanced APIs Reference