🔍 Overview

TensorFlow provides powerful tools for building and training complex models. This guide explores advanced topics to deepen your understanding and improve your machine learning workflows.

📌 Key Concepts

  • Distributed Training 🌐
    Scale models using tf.distribute strategies like MirroredStrategy or TPUStrategy.
    Learn more about distributed computing

  • Custom Training Loops 🧠
    Replace tf.keras with low-level APIs for full control over gradients and optimization.

    Custom_Training_Loop

  • Advanced Model Architectures 🏗️
    Explore techniques like transformers, graph networks, and custom layers.
    Expand your knowledge here

  • Optimization Strategies ⚙️
    Use AdamW, LAMB, or custom learning rate schedules for better convergence.

    Optimization_Strategies

📘 Practical Examples

  1. Multi-GPU Training 
    strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
    model = tf.keras.Sequential([...])
  2. Custom Gradients
    Use tf.GradientTape for manual gradient computation and debugging.
    Custom_Gradients

📚 Further Reading

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