Getting Started with TensorFlow
Chapter 1: TensorFlow Fundamentals and Setup
Introduction to TensorFlow 2.x
Setting Up Your Development Environment
CPU vs GPU Considerations
Verifying Your Installation
The TensorFlow Ecosystem Overview
Quiz for Chapter 1
Chapter 2: Core TensorFlow Concepts
Understanding Tensors
Tensor Operations
Variables in TensorFlow
Automatic Differentiation with GradientTape
Introduction to tf.function
Practice: Tensor Manipulation and Gradients
Quiz for Chapter 2
Chapter 3: Building Models with Keras
Keras: TensorFlow's High-Level API
The Sequential Model API
Common Keras Layers
Activation Functions
The Functional API for Complex Models
Custom Layers and Models (Introduction)
Hands-on Practical: Building a Classifier
Quiz for Chapter 3
Chapter 4: Training and Evaluating Models
Compiling the Model: Loss Functions
Compiling the Model: Optimizers
Compiling the Model: Metrics
Training with model.fit()
Evaluating Model Performance with model.evaluate()
Making Predictions with model.predict()
Using Callbacks during Training
Visualizing Training with TensorBoard
Practice: Training and Monitoring
Quiz for Chapter 4
Chapter 5: Data Input Pipelines with tf.data
Why tf.data?
Creating Datasets from Tensors, NumPy, and Generators
Working with TFRecord Files
Applying Transformations: map()
Batching and Shuffling
Prefetching for Performance
Integrating tf.data with model.fit()
Image Data Augmentation with tf.data
Hands-on Practical: Building an Image Data Pipeline
Quiz for Chapter 5
Chapter 6: Saving and Loading Models
Why Save and Load Models?
Saving Checkpoints during Training
Saving Weights Only
Saving the Entire Model (Architecture + Weights + Optimizer State)
TensorFlow SavedModel Format
Loading Pre-trained Models
Introduction to TensorFlow Hub
Practice: Saving and Restoring Training
Quiz for Chapter 6
Prefetching for Performance
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- Better performance with the tf.data API, TensorFlow Authors, 2023 TensorFlow official documentation - This is the primary official resource for optimizing
tf.datapipelines, coveringprefetch()andAUTOTUNEin detail. - tf.data: A Flexible and Performant Data Input Pipeline for TensorFlow, Ethan Holtz, Anjali Sridhar, Martin Wicke, Emily Newman, Anirudh Raman, Jeremy Hyrkas, Sameer Singh, 2018 2nd Conference on Systems for Machine Learning (SysML) (USENIX) DOI: 10.5555/3295222.3295229 - Introduces the
tf.dataAPI, explaining its design principles for building efficient and flexible data pipelines, including the motivation for operations likeprefetch. - Performance Guide, TensorFlow Authors, 2023 TensorFlow official documentation - This guide provides a high-level overview of various performance optimization techniques in TensorFlow, where
tf.datainput pipeline optimization, including prefetching, is a key component. - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2022 (O'Reilly Media) - A practical guide that extensively covers TensorFlow, including building and optimizing data input pipelines with
tf.data, providing concrete examples and best practices for performance.