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Below is a comprehensive table organizing all deep learning examples for your xtorch library, categorized by domains and tasks, with subcategories and specific example titles. This structure is designed to help users learn xtorch, which aims to rebuild PyTorch, torchvision, torchtext, torchaudio, and all associated models and datasets in C++. The table covers all major deep learning areas, ensuring beginners and advanced users can navigate the xtorch-examples repository effectively.
Comprehensive Table of xtorch Deep Learning Examples
| Category | Subcategory | Example Title | Description |
|---|---|---|---|
| Getting Started | - | Introduction to xtorch: Tensors and Autograd | Demonstrates tensor operations and autograd basics in xtorch. |
| - | Building and Training a Simple Neural Network | Trains a fully connected network on MNIST using xtorch::nn::Sequential. |
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| - | Using the xtorch Trainer for Easy Training | Shows how to use xtorch’s trainer API for simplified training workflows. |
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| Computer Vision | Image Classification | Classifying Handwritten Digits with LeNet on MNIST | Trains a LeNet-5 CNN on MNIST for digit classification. |
| Fine-tuning ResNet on CIFAR-10 | Fine-tunes a ResNet model on CIFAR-10 for image classification. | ||
| Transfer Learning with Pre-trained Models on Custom Datasets | Uses a pre-trained ResNet for transfer learning on a custom image dataset. | ||
| Object Detection | Detecting Objects with Faster R-CNN | Trains Faster R-CNN for object detection on a subset of COCO. | |
| Training YOLOv3 on COCO Dataset | Implements YOLOv3 for real-time object detection on COCO. | ||
| Segmentation | Semantic Segmentation with DeepLabV3 | Trains DeepLabV3 for semantic segmentation on a dataset like PASCAL VOC. | |
| Instance Segmentation with Mask R-CNN | Uses Mask R-CNN for instance segmentation on COCO. | ||
| Image Generation | Generating Images with DCGAN | Trains a DCGAN to generate synthetic images from MNIST. | |
| Style Transfer with CycleGAN | Implements CycleGAN for unpaired image-to-image translation. | ||
| Natural Language Processing | Text Classification | Sentiment Analysis with RNNs | Trains an RNN for sentiment analysis on IMDB dataset. |
| Text Classification with Transformers | Uses a Transformer model for text classification on a custom dataset. | ||
| Sequence to Sequence | Machine Translation with Encoder-Decoder Models | Implements an encoder-decoder model for English-to-French translation. | |
| Summarization with Pointer-Generator Networks | Trains a pointer-generator network for text summarization. | ||
| Language Modeling | Training a GPT-like Model | Builds and trains a small GPT-like model for text generation. | |
| Fine-tuning BERT for Downstream Tasks | Fine-tunes a BERT model for tasks like question answering or classification. | ||
| Audio and Speech | Speech Recognition | End-to-End Speech Recognition with CTC | Trains a CTC-based model for speech recognition on Librispeech. |
| Keyword Spotting with Attention Models | Implements an attention-based model for keyword spotting in audio. | ||
| Audio Classification | Classifying Environmental Sounds | Trains a CNN for environmental sound classification. | |
| Music Genre Classification with Spectrograms | Uses spectrograms and a CNN to classify music genres. | ||
| Time Series and Sequential Data | Forecasting | Time Series Forecasting with LSTMs | Trains an LSTM for time series forecasting on a dataset like stock prices. |
| Multivariate Forecasting with Temporal Fusion Transformers | Uses a Temporal Fusion Transformer for multivariate time series forecasting. | ||
| Anomaly Detection | Detecting Anomalies with Autoencoders | Trains an autoencoder to detect anomalies in time series data. | |
| Using Isolation Forests for Time Series Anomalies | Implements an isolation forest for anomaly detection in sequential data. | ||
| Reinforcement Learning | Value-Based Methods | Q-Learning for FrozenLake Environment | Implements Q-learning for the FrozenLake environment. |
| Deep Q-Networks for Atari Games | Trains a DQN to play Atari games using xtorch. |
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| Policy-Based Methods | REINFORCE Algorithm for CartPole | Implements the REINFORCE algorithm for the CartPole environment. | |
| Proximal Policy Optimization (PPO) for Continuous Control | Uses PPO for continuous control tasks like robotic arm movement. | ||
| Graph Neural Networks | Node-Level Tasks | Node Classification with Graph Convolutional Networks (GCN) | Trains a GCN for node classification on a dataset like Cora. |
| Node Embedding with GraphSAGE | Uses GraphSAGE to generate node embeddings for downstream tasks. | ||
| Graph-Level Tasks | Graph Classification with DiffPool | Implements DiffPool for graph classification on a molecular dataset. | |
| Molecular Property Prediction with Message Passing Neural Networks | Uses MPNNs to predict molecular properties. | ||
| Generative Models | Autoencoders | Denoising Autoencoders for Image Restoration | Trains a denoising autoencoder to restore noisy images. |
| Variational Autoencoders for Latent Space Exploration | Implements a VAE for generating new samples via latent space exploration. | ||
| GANs | Generating MNIST Digits with GANs | Trains a GAN to generate synthetic MNIST digits. | |
| High-Resolution Image Generation with Progressive GANs | Uses Progressive GANs for high-resolution image generation. | ||
| Diffusion Models | Image Generation with DDPM | Implements a Denoising Diffusion Probabilistic Model for image generation. | |
| Deployment and Production | Model Serialization | Saving and Loading Models in xtorch | Demonstrates saving and loading trained models for reuse. |
| Exporting Models to TorchScript | Exports a model to TorchScript for deployment. | ||
| Inference | Building a C++ Application for Model Inference | Creates a C++ app for running inference with a trained model. | |
| Optimizing Inference with TensorRT | Optimizes inference performance using TensorRT integration. | ||
| Web Services | Serving Models with REST APIs | Sets up a REST API to serve xtorch models for remote inference. |
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| Data Handling and Preprocessing | Datasets | Using Built-in Datasets: MNIST, CIFAR-10 | Shows how to load built-in datasets like MNIST and CIFAR-10. |
| Creating Custom Datasets with ImageFolderDataset | Implements a custom dataset class for image folders. | ||
| Data Loaders | Efficient Data Loading with xtorch DataLoader | Demonstrates batching and shuffling with xtorch’s DataLoader. |
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| Transforms | Applying Image Transformations for Augmentation | Applies data augmentation techniques like rotation and flipping. | |
| Optimization and Training Techniques | Optimizers | Training with SGD and Momentum | Trains a model using SGD with momentum. |
| Using AdamW for Better Generalization | Implements AdamW optimizer for improved training. | ||
| Learning Rate Schedulers | Step Decay Learning Rate Scheduler | Uses a step decay scheduler to adjust learning rates. | |
| Cosine Annealing with Warm Restarts | Implements cosine annealing for dynamic learning rate adjustment. | ||
| Regularization | Implementing Dropout in Neural Networks | Adds dropout to a network to prevent overfitting. | |
| Weight Decay for Overfitting Prevention | Applies weight decay during training to regularize the model. | ||
| Performance and Benchmarking | Speed Optimization | Profiling and Optimizing Training Loops | Profiles and optimizes training loops for speed in C++. |
| Using Mixed Precision Training | Implements mixed precision to speed up training with xtorch. |
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| Memory Management | Reducing Memory Usage with Gradient Checkpointing | Uses gradient checkpointing to save memory during training. | |
| Efficient Data Loading to Avoid Bottlenecks | Optimizes data loading to reduce training bottlenecks. | ||
| Distributed and Parallel Training | Data Parallelism | Training on Multiple GPUs with Data Parallelism | Demonstrates data parallelism across multiple GPUs. |
| Model Parallelism | Splitting Models Across GPUs | Splits a large model across GPUs for training. | |
| Distributed Training | Setting Up Distributed Training Across Machines | Sets up distributed training across multiple machines using xtorch. |
Implementation Notes
- Repository Structure: Organize
xtorch-exampleswith a directory for each category (e.g.,computer_vision/,nlp/), subdirectories for subcategories (e.g.,computer_vision/image_classification/), and individual example folders containingmain.cpp,CMakeLists.txt, and aREADME.mdwith setup and running instructions. - User Guidance: The main
README.mdshould list all categories, link to subcategories, and suggest a learning path (e.g., start with "Getting Started"). - C++ Focus: Ensure examples emphasize
xtorch’s C++ strengths, like performance optimization and deployment, with clear comments in the code. - Dependencies: Note that users need LibTorch and
xtorchinstalled, with instructions in each example’s README.
This table provides a structured, comprehensive set of examples to showcase xtorch’s capabilities, aligned with your goal of rebuilding the PyTorch ecosystem. Let me know if you need code snippets for any specific example or further refinements!
Key Citation: xtorch GitHub Repository