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
Why Save and Load Models?
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- Save and load Keras models, Neel Kovelamudi, Francois Chollet, 2024 TensorFlow Documentation - Offers detailed instructions on handling model persistence in TensorFlow and Keras, covering checkpointing, the SavedModel format, and various saving approaches.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A foundational text covering deep learning theory and practice, providing context for model persistence during training, for evaluation, and for deployment.
- MLOps: Machine Learning in Production, Andrew Ng, 2022 Stanford CS230: Deep Learning (Lecture Notes) (DeepLearning.AI) - Explains best practices for managing machine learning models throughout their lifecycle, including model versioning, deployment, and monitoring, which relies on model saving.
- Transfer learning and fine-tuning, TensorFlow Authors, 2024 TensorFlow Tutorials (Google) - Illustrates the technique of transfer learning and fine-tuning, demonstrating how pre-trained models are adapted for new datasets and tasks, a process enabled by model persistence.