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Advanced TensorFlow Techniques

Chapter 1: TensorFlow Execution and Graphs

TensorFlow's Execution Modes: Eager vs. Graph

Understanding tf.function and AutoGraph

Tracing Mechanics and Graph Representation

Implementing Control Flow in Graphs

Automatic Differentiation with tf.GradientTape

Managing Resources and Memory

Debugging TensorFlow Programs

Practice: Optimizing Function Tracing

Chapter 2: High-Performance TensorFlow

Profiling TensorFlow Code with TensorBoard Profiler

Optimizing GPU Utilization

Mixed Precision Training Techniques

Introduction to Tensor Processing Units (TPUs)

XLA (Accelerated Linear Algebra) Compilation

Performance Considerations for tf.data Pipelines

Hands-on Practical: Profiling and Accelerating a Model

Chapter 3: Scaling Training with Distributed Strategies

Fundamentals of Distributed Machine Learning

Overview of tf.distribute.Strategy

MirroredStrategy for Single-Node, Multi-GPU Training

MultiWorkerMirroredStrategy for Multi-Node Training

ParameterServerStrategy Concepts

TPUStrategy for Training on TPUs

Handling Data Parallelism Effectively

Debugging Distributed Training Jobs

Practice: Implementing Distributed Training

Chapter 4: Advanced API Usage and Custom Components

Subclassing tf.keras.Model for Flexibility

Creating Custom tf.keras Layers

Implementing Custom Loss Functions

Developing Custom Metrics

Writing Custom Training Loops

Working with Ragged Tensors and Sparse Tensors

Using TensorFlow Addons for Specialized Operations

Hands-on Practical: Building a Custom Model Pipeline

Chapter 5: Production ML Pipelines with TFX

Introduction to TensorFlow Extended (TFX)

TFX Standard Components Overview

Data Ingestion and Validation

Feature Engineering with Transform

Model Training and Tuning

Model Validation and Analysis

Serving and Deployment with Pusher

Orchestrating TFX Pipelines

Practice: Building a Simple TFX Pipeline

Chapter 6: Model Deployment and Optimization

Saving and Loading Advanced Model Formats

Introduction to TensorFlow Serving

Deploying Models with TF Serving via REST and gRPC

Model Optimization Techniques

Introduction to TensorFlow Lite (TF Lite)

Converting Models for TF Lite

Optimizing for On-Device Inference

Hands-on Practical: Deploying a Model with TF Serving

Chapter 7: Implementing Advanced Architectures

Building Attention Mechanisms from Scratch

Implementing Transformer Blocks

Generative Adversarial Networks (GANs) Concepts

Coding a Simple GAN in TensorFlow

Graph Neural Network (GNN) Basics with TF

Reinforcement Learning Agents with TF-Agents

Practice: Implementing a Transformer Encoder Layer

Advanced TensorFlow Techniques

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:

Advanced

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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