Advanced PyTorch
Chapter 1: PyTorch Internals and Autograd
Tensor Implementation Details
Understanding the Computational Graph
Autograd Engine Mechanics
Custom Autograd Functions: Forward and Backward
Higher-Order Gradient Computation
Inspecting Gradients and Graph Visualization
Memory Management Considerations
Hands-on Practical: Building Custom Autograd Functions
Chapter 2: Advanced Neural Network Architectures
Implementing Transformers from Components
Advanced Attention Mechanisms
Graph Neural Networks with PyTorch Geometric
Normalizing Flows for Generative Modeling
Neural Ordinary Differential Equations
Meta-Learning Algorithms
Practice: Implementing a Custom GNN Layer
Chapter 3: Optimization Techniques and Training Strategies
Sophisticated Optimizers Overview
Advanced Learning Rate Scheduling
Regularization Methods
Gradient Clipping and Accumulation
Mixed-Precision Training with torch.cuda.amp
Strategies for Handling Large Datasets
Automated Hyperparameter Tuning
Hands-on Practical: Implementing Mixed-Precision Training
Chapter 4: Model Deployment and Performance Optimization
TorchScript Fundamentals: Tracing vs Scripting
Model Quantization Techniques
Model Pruning Strategies
Performance Analysis with PyTorch Profiler
Optimizing Kernels with External Libraries
Exporting Models to ONNX Format
Serving Models with TorchServe
Practice: Profiling and Quantizing a Model
Chapter 5: Distributed Training and Parallelism
Fundamental Concepts of Distributed Computing
Data Parallelism with DistributedDataParallel (DDP)
Tensor Model Parallelism
Pipeline Parallelism Implementation
Fully Sharded Data Parallelism (FSDP)
Using torch.distributed Primitives
Setting up Distributed Environments
Hands-on Practical: Setting up a DDP Training Script
Chapter 6: Custom Extensions and Interoperability
Building Custom C++ Extensions
Building Custom CUDA Extensions
Working with the ATen Library
Interfacing PyTorch with NumPy
Extending torch.nn with Custom Modules
Extending torch.optim with Custom Optimizers
Foreign Function Interfaces (FFI)
Practice: Building a Simple CUDA Extension
Automated Hyperparameter Tuning
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- Random Search for Hyper-Parameter Optimization, James Bergstra and Yoshua Bengio, 2012 Journal of Machine Learning Research, Vol. 13 DOI: 10.5555/2330613.2330674 - This paper introduces random search as an effective and computationally efficient alternative to grid search for hyperparameter optimization, especially when only a few hyperparameters are truly influential.
- Practical Bayesian Optimization of Machine Learning Algorithms, Jasper Snoek, Hugo Larochelle, and Ryan P. Adams, 2012 Advances in Neural Information Processing Systems 25 (NIPS 2012), Vol. 25 (Curran Associates, Inc.) DOI: 10.5555/2999486.2999602 - A seminal paper presenting a practical framework for Bayesian optimization using Gaussian processes, demonstrating its superior sample efficiency for tuning machine learning algorithms.
- Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization, Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar, 2017 Journal of Machine Learning Research, Vol. 18 DOI: 10.5555/3305381.3305404 - Introduces Hyperband, an efficient hyperparameter optimization algorithm that intelligently allocates resources using a bandit-based strategy and successive halving to prune unpromising configurations early.
- Optuna: A Next-generation Hyperparameter Optimization Framework, Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama, 2019 Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19) DOI: 10.1145/3292500.3330705 - The original research paper introducing Optuna, an open-source hyperparameter optimization framework with a define-by-run API and efficient sampling and pruning algorithms, specifically mentioned in the section.
- Hyperparameter Optimization: A Review of Methods and Applications, Bob Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando de Freitas, 2016 Proceedings of the IEEE, Vol. 104 (IEEE) DOI: 10.1109/JPROC.2015.2494218 - A comprehensive review paper covering a broad range of hyperparameter optimization techniques, including grid search, random search, and Bayesian optimization, providing a strong theoretical and practical overview.