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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

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

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

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

Chapter 5: Data Input Pipelines with 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

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

Core TensorFlow Concepts

With your TensorFlow environment configured, this chapter introduces the essential building blocks for constructing and training machine learning models. We will cover the primary data structure, the Tensor, along with key operations and concepts like automatic differentiation.

Specifically, you will learn to:

Define and understand TensorFlow Tensors, including their attributes like shape (e.g., $(batch\_size, features)$ ) and data type ( $dtype$ ).
Perform various operations on Tensors, from basic arithmetic to matrix manipulations.
Use tf.Variable to manage mutable state, critical for holding model parameters that change during training.
Calculate gradients using automatic differentiation via tf.GradientTape, the mechanism behind optimizing models.
Convert Python functions into optimized TensorFlow graphs for improved performance using tf.function.

Mastering these concepts provides the necessary foundation for effectively using TensorFlow's higher-level APIs, which we will cover subsequently.

Sections

2.1 Understanding Tensors
2.2 Tensor Operations
2.3 Variables in TensorFlow
2.4 Automatic Differentiation with GradientTape
2.5 Introduction to tf.function
2.6 Practice: Tensor Manipulation and Gradients

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