PyTorch for TensorFlow Developers
Chapter 1: Bridging TensorFlow and PyTorch: Core Concepts
From TensorFlow to PyTorch: A Developer's Roadmap
TensorFlow Graphs versus PyTorch Dynamic Computation
Comparing Tensors: tf.Tensor and torch.Tensor
Fundamental Tensor Operations: A Comparative View
Automatic Differentiation: GradientTape and Autograd Compared
NumPy Integration in PyTorch and TensorFlow
Device Management: CPU and GPU Control
Hands-on Practical: Tensor Manipulations and Autograd
Quiz for Chapter 1
Chapter 2: Building Neural Networks: From Keras to torch.nn
Defining Network Components: Keras Layers and torch.nn.Module
Model Architectures: Keras APIs and PyTorch's nn.Module
Common Layer Types: A Comparative Implementation
Activation Functions: A Comparative Look
Weight Initialization Strategies in PyTorch
Accessing and Modifying Model Parameters and Layers
Hands-on Practical: Constructing Equivalent Models
Quiz for Chapter 2
Chapter 3: Data Loading and Preprocessing: tf.data to torch.utils.data
Data Structures: tf.data.Dataset and torch.utils.data.Dataset
Batching and Iteration: TensorFlow DataLoaders and PyTorch DataLoaders
Data Augmentation: TensorFlow Methods and torchvision.transforms
Implementing Custom Datasets in PyTorch
Preprocessing Data with PyTorch Transforms
Building Efficient Data Pipelines in PyTorch
Hands-on Practical: Creating Custom Datasets and DataLoaders
Quiz for Chapter 3
Chapter 4: Training and Evaluation: Mapping Keras Methods to PyTorch Loops
Training Paradigms: TensorFlow's fit Method and PyTorch Training Loops
Loss Functions in TensorFlow and PyTorch
Optimization Algorithms: TensorFlow and PyTorch Optimizers
Calculating Gradients and Updating Weights in PyTorch
Performance Metrics: Keras Metrics and PyTorch Alternatives
Model Evaluation Loops in PyTorch
Training Control: Keras Callbacks and PyTorch Custom Logic
Hands-on Practical: Implementing a Full Training and Evaluation Loop
Quiz for Chapter 4
Chapter 5: Saving, Loading, and Deploying Models
Model Persistence: TensorFlow Formats and PyTorch state_dict
Saving and Loading Entire Models vs. Only Parameters
Checkpointing Strategies During PyTorch Training
Inspecting Model Architectures and Weights in PyTorch
Introduction to TorchScript for Serialization
Using ONNX for Framework Interoperability
Overview of PyTorch Model Serving with TorchServe
Hands-on Practical: Model Persistence and Basic TorchScript
Quiz for Chapter 5
Chapter 6: Advanced PyTorch Features for TensorFlow Users
Understanding and Utilizing PyTorch Hooks
Distributed Training Approaches
Mixed Precision Training with PyTorch AMP
Profiling PyTorch Code for Performance Bottlenecks
A Glimpse into the PyTorch Ecosystem: torchvision, torchaudio, torchtext
Debugging Strategies for PyTorch Models
Hands-on Practical: Implementing Hooks and Profiling a Model
Quiz for Chapter 6
TensorFlow Graphs versus PyTorch Dynamic Computation
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- PyTorch: An Imperative Style, High-Performance Deep Learning Library, Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala, 2019 Advances in Neural Information Processing Systems, Vol. 32 (Curran Associates, Inc.) DOI: 10.48550/arXiv.1912.01703 - This foundational paper introduces PyTorch, explaining its imperative programming style and dynamic computation graph design, which is central to the 'define-by-run' philosophy.
- Better performance with tf.function, TensorFlow Team, 2023 - The official TensorFlow guide on
tf.functionoffers details on how TensorFlow 2.x compiles Python code into static graphs, including the role of tracing and AutoGraph for performance and deployment. - Autograd: Automatic Differentiation, PyTorch Team, 2017 - This official PyTorch documentation describes the
autogradengine, which dynamically constructs the computation graph for automatic differentiation, providing a practical view of the 'define-by-run' approach. - TensorFlow: A System for Large-Scale Machine Learning, Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, Xiaoqiang Zheng, 2016 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) (USENIX Association) - This seminal paper introduces TensorFlow's architecture, primarily focusing on its original static graph computation model, which provides a historical and architectural background for understanding its evolution.
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