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
Fundamental Tensor Operations: A Comparative View
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- PyTorch Tensors, PyTorch developers, 2024 - The official documentation providing a foundational understanding of
torch.Tensorobjects and their core mathematical, indexing, and reshaping operations. - Tensors, TensorFlow developers, 2024 - TensorFlow's official guide to
tf.Tensorobjects and their operations, serving as a basis for comparison with PyTorch's tensor functionalities. - Deep Learning with PyTorch, Eli Stevens, Luca Antiga, and Thomas Viehmann, 2020 (Manning Publications) - A comprehensive book that provides in-depth explanations of PyTorch fundamentals, including tensor operations and their practical application in deep learning.
- Broadcasting semantics, PyTorch developers, 2021 (PyTorch Foundation) - Official documentation explaining the rules and behavior of broadcasting in PyTorch, which is fundamental for combining tensors of different shapes.
- In-place operations with autograd, PyTorch developers, 2017 (PyTorch Foundation) - A specific note in the PyTorch documentation addressing the nature and considerations of in-place operations, especially their interaction with the autograd engine.
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