Home Blog Courses

All Courses

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

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

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

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

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

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

PyTorch for TensorFlow Developers

Prerequisites: TensorFlow experience required

Level: Intermediate

What You'll Learn

Translate TensorFlow Concepts
Map core TensorFlow components (Tensors, Graphs, Layers, Optimizers) to their PyTorch equivalents.
Master PyTorch's Define-by-Run Paradigm
Understand and utilize PyTorch's dynamic computation graphs for flexible model building.
Develop PyTorch Models
Construct, train, and evaluate neural networks using torch.nn, torch.optim, and custom training loops.
Manage Data Pipelines
Implement efficient data loading and preprocessing pipelines using torch.utils.data and torchvision.transforms.
Handle Model Persistence
Save, load, and prepare PyTorch models for deployment, including an introduction to TorchScript.
Adapt TensorFlow Workflows
Transition existing TensorFlow workflows and thought processes to the PyTorch ecosystem.

© 2025 ApX Machine Learning