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
Common Keras Layers
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- Keras layers API, TensorFlow Developers, 2024 - Official documentation providing detailed information and examples for all Keras layers.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A comprehensive textbook covering the theoretical foundations and mathematical principles of various neural network layers, including Dense, Conv2D, Pooling, Dropout, and LSTMs.
- Gradient-Based Learning Applied to Document Recognition, Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner, 1998 Proceedings of the IEEE, Vol. 86 (IEEE) DOI: 10.1109/5.726791 - Foundational paper introducing Convolutional Neural Networks (LeNet-5) for image recognition, demonstrating the original application of Conv2D and pooling layers.
- Long Short-Term Memory, Sepp Hochreiter and Jürgen Schmidhuber, 1997 Neural Computation, Vol. 9 (MIT Press) DOI: 10.1162/neco.1997.9.8.1735 - The original paper proposing Long Short-Term Memory (LSTM) networks, which address the vanishing gradient problem in recurrent neural networks for sequence processing.
- Dropout: A Simple Way to Prevent Overfitting Neural Networks, Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, 2014 Journal of Machine Learning Research (JMLR), Vol. 15 (JMLR) DOI: 10.5555/2627435.2670313 - Introduces Dropout as a regularization technique to reduce overfitting in neural networks by randomly omitting units during training.