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
Automatic Differentiation with tf.GradientTape
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- tf.GradientTape guide, TensorFlow Authors, 2024 (TensorFlow) - Official TensorFlow guide that details the API and usage of
tf.GradientTapefor automatic differentiation, including specifics like persistent tapes and higher-order gradients. - Automatic Differentiation in Machine Learning: A Survey, Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, Jeffrey Mark Siskind, David Eugene Stewart, Zachary DeVito, James Bradbury, 2018 Journal of Machine Learning Research, Vol. 18 (Microtome Publishing) DOI: 10.5555/3342930.3342931 - A comprehensive academic survey providing a detailed account of automatic differentiation methods, their history, and their widespread application in machine learning, with a focus on reverse-mode AD.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A foundational textbook that thoroughly explains backpropagation, which is a specific instance of reverse-mode automatic differentiation applied to neural networks, and various gradient-based optimization algorithms. Chapters 6 and 8 are particularly relevant.