{"id":25480480,"url":"https://github.com/cycle-sync-ai/deep-learning-project-directory-structure","last_synced_at":"2026-01-24T05:03:46.836Z","repository":{"id":276727834,"uuid":"930099140","full_name":"cycle-sync-ai/deep-learning-project-directory-structure","owner":"cycle-sync-ai","description":"Deep Learning Project Directory Structure","archived":false,"fork":false,"pushed_at":"2025-02-10T04:18:22.000Z","size":3,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-05-18T09:08:03.358Z","etag":null,"topics":["deep","deep-learning","directory-structure","directory-tree","learning","neural-network","project","structure"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/cycle-sync-ai.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-10T04:14:41.000Z","updated_at":"2025-02-10T04:20:04.000Z","dependencies_parsed_at":"2025-02-10T05:24:12.161Z","dependency_job_id":"c4552af2-1889-4d76-8903-30c755eeb188","html_url":"https://github.com/cycle-sync-ai/deep-learning-project-directory-structure","commit_stats":null,"previous_names":["cycle-sync-ai/deep-learning-project-directory-structure"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/cycle-sync-ai/deep-learning-project-directory-structure","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cycle-sync-ai%2Fdeep-learning-project-directory-structure","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cycle-sync-ai%2Fdeep-learning-project-directory-structure/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cycle-sync-ai%2Fdeep-learning-project-directory-structure/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cycle-sync-ai%2Fdeep-learning-project-directory-structure/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cycle-sync-ai","download_url":"https://codeload.github.com/cycle-sync-ai/deep-learning-project-directory-structure/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cycle-sync-ai%2Fdeep-learning-project-directory-structure/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28712841,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-24T05:01:10.984Z","status":"ssl_error","status_checked_at":"2026-01-24T04:59:18.328Z","response_time":89,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep","deep-learning","directory-structure","directory-tree","learning","neural-network","project","structure"],"created_at":"2025-02-18T15:52:56.553Z","updated_at":"2026-01-24T05:03:46.817Z","avatar_url":"https://github.com/cycle-sync-ai.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"```\nproject_name/\n│\n├── data/                          # All data-related directories and files\n│   ├── raw/                       # Original, immutable data dump\n│   ├── processed/                 # Data transformed for modeling, such as TFRecords or HDF5\n│   ├── interim/                   # Temporary processed datasets, possibly smaller or for prototyping\n│\n├── docs/                          # Project documentation\n│   ├── api/                       # API reference\n│   ├── design_docs/               # Design documents, architectural choices\n│   ├── tutorials/                 # Step-by-step guides and tutorials\n│   └── diagrams/                  # Network architecture diagrams, flowcharts\n│\n├── notebooks/                     # Jupyter notebooks\n│   ├── eda/                       # Exploratory Data Analysis notebooks\n│   ├── model_experiments/         # Prototyping and experiments\n│   └── results/                   # Visualization of final results, comparisons\n│\n├── src/                           # Source code\n│   ├── data_pipeline/             # All modules related to the data pipeline\n│   │   ├── sourcing/              # Scripts and utilities for sourcing data\n│   │   │   ├── downloaders/       # Scripts to download data from various sources\n│   │   │   ├── scrapers/          # Web scraping scripts or API-based scripts\n│   │   │   ├── validators/        # Scripts to validate data integrity after sourcing\n│   │   │   └── annotations/       # Tools and scripts related to data annotations or labeling\n│   │   │\n│   │   ├── preprocessing/         # Data preprocessing modules\n│   │   │   ├── cleaners/          # Scripts to clean data, handle missing values, outliers, etc.\n│   │   │   ├── transformers/      # Transformation scripts (e.g., normalization, tokenization)\n│   │   │   ├── encoders/          # Encoding techniques, like one-hot, label encoding, etc.\n│   │   │   └── splitters/         # Scripts to split datasets into train/test/validation sets\n│   │   │\n│   │   ├── augmentations/         # Data augmentation modules\n│   │   │   ├── image/             # Image data augmentation (e.g., rotations, flips)\n│   │   │   ├── text/              # Text data augmentation (e.g., back translation, synonym replacement)\n│   │   │   ├── audio/             # Audio data augmentation (e.g., noise injection, speed change)\n│   │   │   └── utils/             # Common utilities for augmentation\n│   │   │\n│   │   ├── loaders/               # Data loading utilities for feeding to models\n│   │   │   ├── dataset_classes/   # Framework-specific dataset classes (e.g., TensorFlow's tf.data.Dataset, PyTorch's Dataset)\n│   │   │   ├── generators/        # Data generators for on-the-fly data feeding and augmentation\n│   │   │   ├── batchers/          # Batch data and manage batch-related operations\n│   │   │   └── async_loaders/     # Asynchronous data loading, especially useful for large datasets\n│   │   │\n│   │   ├── validation/            # Data validation post-processing\n│   │   │   ├── sanity_checks/     # Scripts to check data sanity post-processing\n│   │   │   ├── statistics/        # Scripts to compute and visualize data statistics\n│   │   │   └── comparers/         # Compare data before and after preprocessing\n│   │   │\n│   │   └── utils/                 # Common utilities for the data pipeline\n│   │       ├── visualization/     # Tools to visualize samples, augmentations, etc.\n│   │       ├── logging/           # Data processing related logging utilities\n│   │       └── helpers/           # Miscellaneous helper functions and scripts\n│   │\n│   ├── models/                    # Neural network models and components\n│   │   ├── architectures/         # Different neural network architectures\n│   │   ├── layers/                # Custom layers\n│   │   ├── losses/                # Custom loss functions\n│   │   └── metrics/               # Custom evaluation metrics\n│   │\n│   ├── training/\n│   │   ├── experiments/           # Specific experiment settings and results\n│   │   │   ├── experiment1/       # Each experiment can have its own subdirectory\n│   │   │   │   ├── config.yml     # Configuration file for the experiment\n│   │   │   │   ├── logs/          # Training logs for this experiment\n│   │   │   │   └── results/       # Resulting plots, performance metrics, etc.\n│   │   │   ├── experiment2/\n│   │   │   └── ...\n│   │   │\n│   │   ├── scripts/               # Actual scripts to run training\n│   │   │   ├── train_model.py     # Main training script\n│   │   │   ├── validate_model.py  # Validation script\n│   │   │   ├── resume_training.py # Script to resume training from checkpoints\n│   │   │   └── distributed_train.py # For distributed and parallel training setups\n│   │   │\n│   │   ├── hyperparameters/       # Hyperparameter tuning and optimization\n│   │   │   ├── search_algos/      # Algorithms for hyperparameter search\n│   │   │   │   ├── grid_search.py\n│   │   │   │   ├── random_search.py\n│   │   │   │   └── bayesian_optimization.py\n│   │   │   ├── search_spaces/     # Definitions of hyperparameter search spaces\n│   │   │   └── tuning_results/    # Results from hyperparameter tuning runs\n│   │   │\n│   │   ├── callbacks/             # Custom callbacks used during training\n│   │   │   ├── lr_schedulers/     # Learning rate scheduling callbacks\n│   │   │   ├── early_stopping.py  # Early stopping callback\n│   │   │   ├── model_checkpointing.py # Save model checkpoints during training\n│   │   │   └── tensorboard_logging.py # Log metrics and other details to TensorBoard\n│   │   │\n│   │   ├── strategies/            # Training strategies for different setups\n│   │   │   ├── single_gpu_strategy.py # Training strategy for a single GPU setup\n│   │   │   ├── multi_gpu_strategy.py  # Multi-GPU training strategy\n│   │   │   └── tpu_strategy.py    # TPU training strategy\n│   │   │\n│   │   ├── metrics/               # Metrics to evaluate model during training\n│   │   │   ├── accuracy.py        # Accuracy metric\n│   │   │   ├── f1_score.py        # F1 Score\n│   │   │   └── custom_metric.py   # Any other custom metrics\n│   │   │\n│   │   └── utils/                 # Miscellaneous utilities for training\n│   │       ├── gradient_clipping.py   # Gradient clipping utilities\n│   │       ├── weight_initialization.py # Weight initialization strategies\n│   │       └── mixed_precision.py # Mixed precision training utilities\n│   │\n│   ├── inference/                 # Inference related modules\n│   │   ├── deployment/            # Deployment related utilities\n│   │   │   ├── docker/            # Docker files for containerized deployment\n│   │   │   ├── cloud_functions/   # Cloud function code for serverless deployment\n│   │   │   ├── api_endpoints/     # RESTful API endpoints\n│   │   │   └── edge_devices/      # Deployment scripts for edge devices (like Raspberry Pi, etc.)\n│   │   │\n│   │   ├── tools/                # Inference tools\n│   │   │   ├── model_converters/  # Convert models to different formats (ONNX, TensorFlow Lite, etc.)\n│   │   │   ├── benchmarking/      # Benchmarking the model's performance in real-world scenarios\n│   │   │   └── visualization/     # Visualize predictions, attention maps, etc.\n│   │   │\n│   │   └── utils/                 # Common utilities for inference\n│   │       ├── preprocessing/     # Preprocess input data for inference\n│   │       ├── postprocessing/    # Convert raw model outputs to interpretable results\n│   │       └── logging/           # Inference related logging utilities\n│   │\n│   ├── testing/\n│   │   ├── unit_tests/            # Unit tests for individual functions and components\n│   │   │   ├── data_pipeline_tests/\n│   │   │   ├── model_tests/\n│   │   │   └── training_utils_tests/\n│   │   │\n│   │   ├── integration_tests/     # Tests that check the interactions between modules\n│   │   │   ├── pipeline_integration/\n│   │   │   └── model_training_integration/\n│   │   │\n│   │   └── utils/                 # Utilities for testing\n│   │       ├── fixtures/          # Sample data, models, etc. to be used during tests\n│   │       ├── mockers/           # Mock functions, classes, etc. for testing\n│   │       └── visualization/     # Visualize results, errors, etc. during tests\n│   │\n│   └── utils/                     # Miscellaneous utilities\n│       ├── file_handlers/         # Handle file read/write operations\n│       ├── visualization/         # Common visualization tools\n│       └── others/                # Any other miscellaneous utilities\n│\n├── results/                       # Final results and outputs\n│   ├── models/                    # Trained models, weights, checkpoints\n│   ├── plots/                     # Resulting plots and visualizations\n│   └── tables/                    # Resulting tables, usually in CSV or Excel format\n│\n├── config/                        # Configuration files and environment settings\n│   ├── environment.yml            # Conda environment file\n│   ├── requirements.txt           # Python package requirements file\n│   └── model_config.yml           # Model and training configuration file\n│\n└── README.md                      # Overview and instructions for the project\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcycle-sync-ai%2Fdeep-learning-project-directory-structure","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcycle-sync-ai%2Fdeep-learning-project-directory-structure","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcycle-sync-ai%2Fdeep-learning-project-directory-structure/lists"}