Welcome to PyVerse by Tushar Aggarwal, your ultimate guide to exploring and mastering Python frameworks! This repository is designed to help you navigate the vast landscape of Python frameworks and understand their features, use cases, and best practices.
Python has emerged as one of the most popular programming languages in recent years, thanks to its simplicity, versatility, and vast ecosystem. One of the key factors contributing to Python's success is its rich collection of frameworks that cater to various domains and purposes. PyVerse aims to provide a comprehensive overview of these frameworks, empowering you to make informed decisions and leverage the right tools for your projects.
PyVerse covers a wide range of Python frameworks, including web frameworks, machine learning frameworks, data analysis frameworks, game development frameworks, and more. You'll find detailed information, tutorials, and code examples for each framework.
Each framework in PyVerse is accompanied by comprehensive documentation, providing an overview of its core concepts, installation instructions, usage examples, and tips for effective development.
PyVerse goes beyond theory by offering practical examples and sample projects that demonstrate the capabilities of various Python frameworks. You can explore these examples to gain practical experience and learn how to apply the frameworks in real-world scenarios.
We believe in the power of collaboration, and PyVerse welcomes contributions from the community. Whether you want to fix a bug, improve documentation, or add a new framework, your contributions are highly valued. Please refer to our contribution guidelines to get started.
To get started with PyVerse, follow these steps:
- Explore the frameworks directory to discover the available Python frameworks. Each framework has its own directory containing documentation, tutorials, and example code.
- Dive into the documentation and examples of the frameworks you are interested in. Experiment with the code and explore the capabilities of each framework.
We have an exciting roadmap planned for PyVerse:
- Adding more Python frameworks to expand the coverage and provide a more comprehensive resource.
- Enhancing the existing documentation and examples based on community feedback and emerging best practices.
- Organizing webinars, workshops, and live coding sessions to facilitate learning and foster a thriving community.
- Creating video tutorials to supplement the written documentation and provide a more immersive learning experience.
Stay tuned for updates!
Join the PyVerse community to connect with fellow developers, ask questions, share your experiences, and stay updated with the latest developments. Contact @ info@tushar-aggarwal.com
| S.no. | Used for | Lib, Framework & Tech |
|---|---|---|
| 1 | Data Science | Pandas |
| 2 | Data Science | Numpy |
| 3 | Data Science | Seaborn |
| 4 | Data Science | Scipy |
| 5 | Data Science | Matplotlib |
| 6 | Machine Learning | Scikit-learn |
| 7 | Machine Learning | Pytorch |
| 8 | Machine Learning | Tensorflow |
| 9 | Machine Learning | Xgboost |
| 10 | Machine Learning | Lightgbm |
| 12 | Machine Learning | Keras |
| 13 | Machine Learning | Pycaret |
| 14 | MLOps | Mlflow |
| 15 | MLOps | Kubeflow |
| 16 | MLOps | Zenml |
| 17 | Explainable AI | Shap |
| 18 | Explainable AI | Lime |
| 19 | Explainable AI | Interpretml |
| 20 | Text Processing | Spacy |
| 21 | Text Processing | Nltk |
| 22 | Text Processing | Textblob |
| 23 | Text Processing | Corenlp |
| 24 | Text Processing | Gensim |
| 25 | Text Processing | Regex |
| 26 | Image Processing | Opencv |
| 27 | Image Processing | Scikit-image |
| 28 | Image Processing | Pillow |
| 29 | Image Processing | Mahotas |
| 30 | Image Processing | Simpleitk |
| 31 | Web Framework | Flask |
| 32 | Web Framework | Fastapi |
| 33 | Web Framework | Django |
| 34 | Web Framework | Dash |
| 35 | Web Framework | Pyramid |
| 36 | Web Scraping | Beautifulsoup |
| 37 | Web Scraping | Scrapy |
| 38 | Web Scraping | Selenium |
| 39 | Data Visualization | Plotly |
| 40 | Data Visualization | Tableau |
| 41 | Data Visualization | Bokeh |
| 42 | Data Visualization | Ggplot |
| 43 | Natural Language Processing | Transformer |
| 44 | Natural Language Processing | BERT |
| 45 | Natural Language Processing | Word2Vec |
| 46 | Natural Language Processing | GloVe |
| 47 | Natural Language Processing | ELMo |
| 48 | Reinforcement Learning | OpenAI Gym |
| 49 | Reinforcement Learning | Dopamine |
| 50 | Reinforcement Learning | PyBullet |
| 51 | Reinforcement Learning | Stable Baselines3 |
| 52 | Cloud Computing | AWS |
| 53 | Cloud Computing | Google Cloud |
| 54 | Cloud Computing | Microsoft Azure |
| 55 | Cloud Computing | IBM Cloud |
| 56 | Cloud Computing | Heroku |
| 57 | Database Management | MySQL |
| 58 | Database Management | PostgreSQL |
| 59 | Database Management | MongoDB |
| 60 | Database Management | SQLite |
| 61 | Database Management | Oracle Database |
| 62 | Deep Learning | Convolutional Neural Networks (CNN) |
| 63 | Deep Learning | Recurrent Neural Networks (RNN) |
| 64 | Deep Learning | Generative Adversarial Networks (GANs) |
| 65 | Deep Learning | Autoencoders |
| 66 | Deep Learning | Transfer Learning |
| 67 | Time Series Analysis | ARIMA |
| 68 | Time Series Analysis | Prophet |
| 69 | Time Series Analysis | Exponential Smoothing |
| 70 | Time Series Analysis | SARIMA |
| 71 | Time Series Analysis | Seasonal Decomposition of Time Series (STL) |
| 72 | Statistics | Hypothesis Testing |
| 73 | Statistics | Regression Analysis |
| 74 | Statistics | ANOVA |
| 75 | Statistics | Bayesian Inference |
| 76 | Statistics | Principal Component Analysis (PCA) |
| 77 | Computer Vision | Object Detection |
| 78 | Computer Vision | Image Segmentation |
| 79 | Computer Vision | Face Recognition |
| 80 | Computer Vision | Optical Character Recognition (OCR) |
| 81 | Computer Vision | Instance Segmentation |
| 82 | Recommender Systems | Collaborative Filtering |
| 83 | Recommender Systems | Content-Based Filtering |
| 84 | Recommender Systems | Matrix Factorization |
| 85 | Recommender Systems | Association Rules |
| 86 | Recommender Systems | Deep Learning for Recommender Systems |
| 87 | Unsupervised Learning | K-means Clustering |
| 88 | Unsupervised Learning | Hierarchical Clustering |
| 89 | Unsupervised Learning | Dimensionality Reduction |
| 90 | Unsupervised Learning | Association Rules Mining |
| 91 | Unsupervised Learning | Gaussian Mixture Models (GMM) |
| 92 | Network Analysis | Graph Theory |
| 93 | Network Analysis | Social Network Analysis |
| 94 | Network Analysis | Community Detection |
| 95 | Network Analysis | Link Prediction |
| 96 | Network Analysis | Centrality Measures |
| 97 | Cloud Computing | Amazon S3 |
| 98 | Cloud Computing | Docker |
| 99 | Cloud Computing | Kubernetes |
| 100 | Cloud Computing | Serverless Computing |
| 101 | Natural Language Processing | Named Entity Recognition (NER) |
| 102 | Natural Language Processing | Sentiment Analysis |
| 103 | Natural Language Processing | Topic Modeling |
| 104 | Natural Language Processing | Dependency Parsing |
| 105 | Natural Language Processing | Text Classification |
| 106 | Recommender Systems | Hybrid Recommender Systems |
| 107 | Recommender Systems | Contextual Recommender Systems |
| 108 | Recommender Systems | Sequential Recommender Systems |
| 109 | Recommender Systems | Factorization Machines |
| 110 | Recommender Systems | Deep Reinforcement Learning for Recommender Systems |
| 111 | Optimization | Linear Programming |
| 112 | Optimization | Nonlinear Programming |
| 113 | Optimization | Integer Programming |
| 114 | Optimization | Convex Optimization |
| 115 | Optimization | Heuristic Optimization |
| 116 | Distributed Systems | Apache Hadoop |
| 117 | Distributed Systems | Apache Spark |
| 118 | Distributed Systems | Apache Kafka |
| 119 | Distributed Systems | Apache Flink |
| 120 | Distributed Systems | Apache Cassandra |
| 121 | Natural Language Generation | Template-Based Generation |
| 122 | Natural Language Generation | Rule-Based Generation |
| 123 | Natural Language Generation | Statistical Language Generation |
| 124 | Natural Language Generation | Neural Language Generation |
| 125 | Natural Language Generation | Content Planning |
| 126 | Reinforcement Learning | Monte Carlo Methods |
| 127 | Reinforcement Learning | Q-Learning |
| 128 | Reinforcement Learning | Policy Gradient Methods |
| 129 | Reinforcement Learning | Actor-Critic Methods |
| 130 | Reinforcement Learning | Model-Based Reinforcement Learning |
| 131 | Robotics | ROS (Robot Operating System) |
| 132 | Robotics | SLAM (Simultaneous Localization and Mapping) |
| 133 | Robotics | Inverse Kinematics |
| 134 | Robotics | Reinforcement Learning for Robotics |
| 135 | Robotics | Motion Planning |
| 136 | Big Data Processing | Apache Hive |
| 137 | Big Data Processing | Apache Pig |
| 138 | Big Data Processing | Apache Storm |
| 139 | Big Data Processing | Apache Beam |
| 140 | Big Data Processing | Apache Flume |
| 141 | Generative Models | Variational Autoencoders (VAEs) |
| 142 | Generative Models | Generative Adversarial Networks (GANs) |
| 143 | Generative Models | Flow-based Models |
| 144 | Generative Models | Auto-regressive Models |
| 145 | Generative Models | Generative Adversarial Networks (GANs) |
| 146 | Blockchain | Bitcoin |
| 147 | Blockchain | Ethereum |
| 148 | Blockchain | Smart Contracts |
| 149 | Blockchain | Distributed Ledger Technology |
| 150 | Blockchain | Consensus Algorithms |
| 151 | Natural Language Processing | Word Embeddings |
| 152 | Natural Language Processing | Attention Mechanisms |
| 153 | Natural Language Processing | Sequence-to-Sequence Models |
| 154 | Natural Language Processing | Language Translation |
| 155 | Natural Language Processing | Question Answering |
| 156 | Model Interpretability | Feature Importance |
| 157 | Model Interpretability | Partial Dependence Plots |
| 158 | Model Interpretability | Gradient Boosting Interpretation |
| 159 | Model Interpretability | Local Interpretable Model-Agnostic Explanations (LIME) |
| 160 | Model Interpretability | SHapley Additive exPlanations (SHAP) |
| 161 | Bayesian Methods | Bayesian Networks |
| 162 | Bayesian Methods | Gibbs Sampling |
| 163 | Bayesian Methods | Metropolis-Hastings Algorithm |
| 164 | Bayesian Methods | Variational Inference |
| 165 | Bayesian Methods | Bayesian Optimization |
| 166 | Geospatial Analysis | GIS (Geographic Information System) |
| 167 | Geospatial Analysis | Spatial Data Visualization |
| 168 | Geospatial Analysis | Spatial Clustering |
| 169 | Geospatial Analysis | Spatial Regression |
| 170 | Geospatial Analysis | Spatial Interpolation |
| 171 | Cloud Computing | OpenStack |
| 172 | Cloud Computing | Rackspace Cloud |
| 173 | Cloud Computing | VMware vCloud |
| 174 | Cloud Computing | Alibaba Cloud |
| 175 | Cloud Computing | Red Hat OpenShift |
| 176 | Natural Language Processing | Dependency Parsing |
| 177 | Natural Language Processing | Named Entity Recognition (NER) |
| 178 | Natural Language Processing | Semantic Role Labeling |
| 179 | Natural Language Processing | Sentiment Analysis |
| 180 | Natural Language Processing | Coreference Resolution |
| 181 | Graph Neural Networks | Graph Convolutional Networks (GCNs) |
| 182 | Graph Neural Networks | Graph Attention Networks (GATs) |
| 183 | Graph Neural Networks | GraphSAGE |
| 184 | Graph Neural Networks | Relational Graph Convolutional Networks (RGCNs) |
| 185 | Graph Neural Networks | Graph Autoencoders |
| 186 | Feature Engineering | Feature Scaling |
| 187 | Feature Engineering | Feature Selection |
| 188 | Feature Engineering | Feature Extraction |
| 189 | Feature Engineering | Feature Encoding |
| 190 | Feature Engineering | Feature Crossing |
| 191 | Privacy and Security | Federated Learning |
| 192 | Privacy and Security | Differential Privacy |
| 193 | Privacy and Security | Encrypted ML |
| 194 | Privacy and Security | Privacy-Preserving Data Mining |
| 195 | Privacy and Security | Adversarial Attacks and Defenses |
| 196 | Causal Inference | Causal Graphs |
| 197 | Causal Inference | Pearl's Causal Inference Framework |
| 198 | Causal Inference | Instrumental Variables |
| 199 | Causal Inference | Propensity Score Matching |
| 200 | Causal Inference | Causal Forests |
| 201 | Automated ML | AutoML |
| 202 | Automated ML | Model Selection |
| 203 | Automated ML | Feature Engineering Automation |
| 204 | Automated ML | Hyperparameter Optimization |
| 205 | Automated ML | Ensemble Methods |
| 206 | Fraud Detection | Fraud Analytics |
| 207 | Fraud Detection | Anomaly Detection |
| 208 | Fraud Detection | Social Network Analysis for Fraud Detection |
| 209 | Fraud Detection | Behavioral Analytics |
| 210 | Fraud Detection | ML for Fraud Detection |
| 211 | Hyperparameter Tuning | Grid Search |
| 212 | Hyperparameter Tuning | Random Search |
| 213 | Hyperparameter Tuning | Bayesian Optimization |
| 214 | Hyperparameter Tuning | Genetic Algorithms |
| 215 | Hyperparameter Tuning | Optuna |
| 216 | Adversarial ML | Adversarial Attacks |
| 217 | Adversarial ML | Adversarial Defenses |
| 218 | Adversarial ML | Generative Adversarial Networks (GANs) for Adversarial Learning |
| 219 | Adversarial ML | Transferability of Adversarial Examples |
| 220 | Adversarial ML | Adversarial Robustness Testing |
| 221 | Time Series Forecasting | ARIMA |
| 222 | Time Series Forecasting | Exponential Smoothing |
| 223 | Time Series Forecasting | Prophet |
| 224 | Time Series Forecasting | Long Short-Term Memory (LSTM) |
| 225 | Time Series Forecasting | Seasonal Autoregressive Integrated Moving Average (SARIMA) |
| 226 | Causality Discovery | Constraint-based Causal Discovery |
| 227 | Causality Discovery | Information-Theoretic Causal Discovery |
| 228 | Causality Discovery | Granger Causality |
| 229 | Causality Discovery | Structural Equation Modeling |
| 230 | Causality Discovery | Counterfactual Reasoning |
| 231 | Recommender Systems | Deep Reinforcement Learning for Recommender Systems |
| 232 | Recommender Systems | Knowledge-Based Recommender Systems |
| 233 | Recommender Systems | Context-Aware Recommender Systems |
| 234 | Recommender Systems | Sequence-Aware Recommender Systems |
| 235 | Recommender Systems | Meta-Learning for Recommender Systems |
| 236 | Transfer Learning | Domain Adaptation |
| 237 | Transfer Learning | Model Distillation |
| 238 | Transfer Learning | Progressive Neural Networks |
| 239 | Transfer Learning | Taskonomy |
| 240 | Transfer Learning | Cross-Domain Knowledge Transfer |
| 241 | Explainable AI | Counterfactual Explanations |
| 242 | Explainable AI | Model-Agnostic Explanations |
| 243 | Explainable AI | Rule-Based Explanations |
| 244 | Explainable AI | Anchors |
| 245 | Explainable AI | Causal Explanations |
| 246 | Time Series Anomaly Detection | One-Class SVM |
| 247 | Time Series Anomaly Detection | Isolation Forest |
| 248 | Time Series Anomaly Detection | Autoencoders for Anomaly Detection |
| 249 | Time Series Anomaly Detection | Change Point Detection |
| 250 | Time Series Anomaly Detection | Seasonal Hybrid ESD (S-H-ESD) |
| 251 | Cybersecurity | Network Intrusion Detection |
| 252 | Cybersecurity | Malware Detection |
| 253 | Cybersecurity | Behavioral Analytics for Cybersecurity |
| 254 | Cybersecurity | Vulnerability Assessment |
| 255 | Cybersecurity | Security Information and Event Management (SIEM) |
| 256 | Adversarial Reinforcement Learning | Adversarial Environments |
| 257 | Adversarial Reinforcement Learning | Adversarial Policy Optimization |
| 258 | Adversarial Reinforcement Learning | Inverse Reinforcement Learning |
| 259 | Adversarial Reinforcement Learning | Adversarial Multi-Agent Systems |
| 260 | Adversarial Reinforcement Learning | Robust Reinforcement Learning |
| 261 | Generative Adversarial Networks | Conditional GANs |
| 262 | Generative Adversarial Networks | Wasserstein GANs |
| 263 | Generative Adversarial Networks | CycleGAN |
| 264 | Generative Adversarial Networks | StyleGAN |
| 265 | Generative Adversarial Networks | Latent Space Interpolation |
| 266 | Federated Learning | Federated Averaging |
| 267 | Federated Learning | Differential Privacy in Federated Learning |
| 268 | Federated Learning | Vertical Federated Learning |
| 269 | Federated Learning | Federated Transfer Learning |
| 270 | Federated Learning | Federated Meta-Learning |
| 271 | Federated Learning | Federated Learning on Edge Devices |
| 272 | Quantum ML | Quantum Computing Basics |
| 273 | Quantum ML | Quantum Neural Networks |
| 274 | Quantum ML | Variational Quantum Algorithms |
| 275 | Quantum ML | Quantum Support Vector Machines |
| 276 | Quantum ML | Quantum Data Encoding |
| 277 | Reinforcement Learning for Robotics | Learning from Demonstrations |
| 278 | Reinforcement Learning for Robotics | Sim2Real Transfer |
| 279 | Reinforcement Learning for Robotics | Model-Based Reinforcement Learning for Robotics |
| 280 | Reinforcement Learning for Robotics | Imitation Learning |
| 281 | Reinforcement Learning for Robotics | Robotic Manipulation |
| 282 | Generative Models for text | Transformer-Based Language Models |
| 283 | Generative Models for text | GPT (Generative Pre-trained Transformer) |
| 284 | Generative Models for text | LSTM-Based Language Models |
| 285 | Generative Models for text | BERT (Bidirectional Encoder Representations from Transformers) |
| 286 | Generative Models for text | VAE (Variational Autoencoder) for Text Generation |
| 287 | Multi-Modal Learning | Multi-Modal Fusion |
| 288 | Multi-Modal Learning | Cross-Modal Retrieval |
| 289 | Multi-Modal Learning | Multi-Modal Generative Models |
| 290 | Multi-Modal Learning | Multi-Modal Reinforcement Learning |
| 291 | Multi-Modal Learning | Multi-Modal Sentiment Analysis |
| 292 | Model Compression | Pruning |
| 293 | Model Compression | Quantization |
| 294 | Model Compression | Distillation |
| 295 | Model Compression | Weight Sharing |
| 296 | Model Compression | Knowledge Distillation |
| 297 | Automated Planning | and scheduling |
| 298 | Automated Planning | and scheduling |
| 299 | Automated Planning | and scheduling |
| 300 | Automated Planning | and scheduling |
| 301 | Automated Planning | and scheduling |
| 302 | Domain Adaptation | Domain-Adversarial Training |
| 303 | Domain Adaptation | CycleGAN for Domain Adaptation |
| 304 | Domain Adaptation | Transfer Learning for Domain Adaptation |
| 305 | Domain Adaptation | Unsupervised Domain Adaptation |
| 306 | Domain Adaptation | Partial Domain Adaptation |
| 307 | Active Learning | Uncertainty Sampling |
| 308 | Active Learning | Query-by-Committee |
| 309 | Active Learning | Expected Model Change |
| 310 | Active Learning | Diversity Sampling |
| 311 | Active Learning | Budgeted Active Learning |
| 312 | Automated Feature Engineering | Featuretools |
| 313 | Automated Feature Engineering | Deep Feature Synthesis |
| 314 | Automated Feature Engineering | Genetic Programming |
| 315 | Automated Feature Engineering | Automated Feature Extraction |
| 316 | Automated Feature Engineering | Feature Encoding |
| 317 | Adversarial Attacks | White-Box Attacks |
| 318 | Adversarial Attacks | Black-Box Attacks |
| 319 | Adversarial Attacks | Physical Attacks |
| 320 | Adversarial Attacks | Transfer Attacks |
| 321 | Adversarial Attacks | Evasion Attacks |
| 322 | Federated Reinforcement Learning | Federated Q-Learning |
| 323 | Federated Reinforcement Learning | Federated Policy Gradient |
| 324 | Federated Reinforcement Learning | Federated Actor-Critic |
| 325 | Federated Reinforcement Learning | Federated Proximal Policy Optimization (PPO) |
| 326 | Federated Reinforcement Learning | Federated Multi-Agent Reinforcement Learning |
| 327 | Federated Reinforcement Learning | Federated Inverse Reinforcement Learning |
| 328 | Graph Embedding | DeepWalk |
| 329 | Graph Embedding | Node2Vec |
| 330 | Graph Embedding | Graph Convolutional Network (GCN) Embedding |
| 331 | Graph Embedding | Tensor Factorization |
| 332 | Graph Embedding | Heterogeneous Graph Embedding |
| 333 | Anomaly Detection in Graphs | Graph-based Outlier Detection |
| 334 | Anomaly Detection in Graphs | Spectral Clustering |
| 335 | Anomaly Detection in Graphs | Isolation Forest for Graphs |
| 336 | Anomaly Detection in Graphs | Collective Anomaly Detection |
| 337 | Anomaly Detection in Graphs | Community Structure-based Anomaly Detection |
| 338 | Interpretable ML | Partial Dependence Plots (PDP) |
| 339 | Interpretable ML | Feature Importance Ranking |
| 340 | Interpretable ML | Interpretable Rule-based Models |
| 341 | Interpretable ML | Local Interpretable Model-agnostic Explanations (LIME) |
| 342 | Interpretable ML | Shapley Values |
| 343 | Generative Models for images | DCGAN (Deep Convolutional GAN) |
| 344 | Generative Models for images | StyleGAN2 |
| 345 | Generative Models for images | CycleGAN for Image Translation |
| 346 | Generative Models for images | Variational Autoencoders (VAEs) for Image Generation |
| 347 | Generative Models for images | Progressive Growing of GANs (PGGAN) |
| 348 | Model Selection | Train-Validation-Test Split |
| 349 | Model Selection | Cross-Validation |
| 350 | Model Selection | Holdout Validation |
| 351 | Model Selection | Grid Search Cross-Validation |
| 352 | Model Selection | Randomized Search Cross-Validation |
| 353 | Model Selection | Model Selection using Information Criteria |
| 354 | Model Selection | Ensemble Model Selection |
| 355 | Continuous Learning | Lifelong Learning |
| 356 | Continuous Learning | Incremental Learning |
| 357 | Continuous Learning | Online Learning |
| 358 | Continuous Learning | Dynamic Model Expansion |
| 359 | Continuous Learning | Concept Drift Detection |
| 360 | Continuous Learning | Transfer Learning in Dynamic Environments |
| 361 | Federated Learning for healthcare | Federated Learning for Electronic Health Records (EHR) |
| 362 | Federated Learning for healthcare | Privacy-Preserving ML in Healthcare |
| 363 | Federated Learning for healthcare | Federated Learning for Clinical Decision Support Systems |
| 364 | Federated Learning for healthcare | Federated Learning for Disease Prediction |
| 365 | Federated Learning for healthcare | Federated Learning for Medical Imaging Analysis |
| 366 | Model Distillation | for lightweight models |
| 367 | Model Distillation | for lightweight models |
| 368 | Model Distillation | for lightweight models |
| 369 | Model Distillation | for lightweight models |
| 370 | Model Distillation | for lightweight models |
| 371 | Time Series Analysis for forecasting | Seasonal ARIMA |
| 372 | Time Series Analysis for forecasting | Vector Autoregression (VAR) |
| 373 | Time Series Analysis for forecasting | Long Short-Term Memory (LSTM) with Attention |
| 374 | Time Series Analysis for forecasting | Prophet with Holidays |
| 375 | Time Series Analysis for forecasting | Ensemble Methods for Time Series Forecasting |
| 376 | Privacy-Preserving ML | Secure Multi-Party Computation (MPC) |
| 377 | Privacy-Preserving ML | Homomorphic Encryption |
| 378 | Privacy-Preserving ML | Differential Privacy Mechanisms |
| 379 | Privacy-Preserving ML | Federated Learning with Differential Privacy |
| 380 | Privacy-Preserving ML | Garbled Circuits |
| 381 | Image Segmentation | U-Net |
| 382 | Image Segmentation | Mask R-CNN |
| 383 | Image Segmentation | Fully Convolutional Networks (FCN) |
| 384 | Image Segmentation | Semantic Segmentation |
| 385 | Image Segmentation | Instance Segmentation |
| 386 | Deep Reinforcement Learning | DQN (Deep Q-Network) |
| 387 | Deep Reinforcement Learning | DDPG (Deep Deterministic Policy Gradient) |
| 388 | Deep Reinforcement Learning | PPO (Proximal Policy Optimization) |
| 389 | Deep Reinforcement Learning | A3C (Asynchronous Advantage Actor-Critic) |
| 390 | Deep Reinforcement Learning | SAC (Soft Actor-Critic) |
| 391 | Computer Vision tasks | Object Detection |
| 392 | Computer Vision tasks | Image Classification |
| 393 | Computer Vision tasks | Image Segmentation |
| 394 | Computer Vision tasks | Face Recognition |
| 395 | Computer Vision tasks | Instance Segmentation |
| 396 | Model Evaluation | Mean Absolute Error (MAE) |
| 397 | Model Evaluation | Mean Squared Error (MSE) |
| 398 | Model Evaluation | R-Squared (R2) Score |
| 399 | Model Evaluation | Accuracy |
| 400 | Model Evaluation | Precision and Recall |
| 401 | Natural Language Generation | Text-to-Text Transfer Transformer (T5) |
| 402 | Natural Language Generation | GPT-3 (Generative Pre-trained Transformer 3) |
| 403 | Natural Language Generation | Language Modeling |
| 404 | Natural Language Generation | Conditional Text Generation |
| 405 | Natural Language Generation | Neural Machine Translation |
| 406 | Sentiment Analysis | Supervised Sentiment Analysis |
| 407 | Sentiment Analysis | Aspect-Based Sentiment Analysis |
| 408 | Sentiment Analysis | Emotion Detection |
| 409 | Sentiment Analysis | Opinion Mining |
| 410 | Sentiment Analysis | Voice of Customer Analysis |
| 411 | Social Network Analysis | Social Network Visualization |
| 412 | Social Network Analysis | Community Detection |
| 413 | Social Network Analysis | Influence Analysis |
| 414 | Social Network Analysis | Link Prediction |
| 415 | Social Network Analysis | Social Network Simulation |
| 416 | Graph Neural Networks | Graph Convolutional Networks (GCNs) |
| 417 | Graph Neural Networks | Graph Attention Networks (GAT) |
| 418 | Graph Neural Networks | GraphSAGE (Graph Sample and Aggregated) |
| 419 | Graph Neural Networks | Graph Isomorphism Networks (GIN) |
| 420 | Graph Neural Networks | Graph Neural Networks for Node Classification |
| 421 | Automated Machine Translation | Sequence-to-Sequence Models |
| 422 | Automated Machine Translation | Neural Machine Translation (NMT) |
| 423 | Automated Machine Translation | Transformer Models for Machine Translation |
| 424 | Automated Machine Translation | Statistical Machine Translation |
| 425 | Automated Machine Translation | Phrase-Based Machine Translation |
| 426 | Automated Machine Translation | Bidirectional Encoder Representations from Transformers (BERT) |
| 427 | Unsupervised Learning | K-Means Clustering |
| 428 | Unsupervised Learning | Hierarchical Clustering |
| 429 | Unsupervised Learning | Principal Component Analysis (PCA) |
| 430 | Unsupervised Learning | Generative Adversarial Networks (GANs) |
| 431 | Unsupervised Learning | Autoencoders |
| 432 | Speech Recognition | Automatic Speech Recognition (ASR) |
| 433 | Speech Recognition | Deep Speech Recognition Models |
| 434 | Speech Recognition | Connectionist Temporal Classification (CTC) |
| 435 | Speech Recognition | Recurrent Neural Networks (RNNs) for Speech Recognition |
| 436 | Speech Recognition | Transformer Models for Speech Recognition |
| 437 | Recommender Systems | Collaborative Filtering |
| 438 | Recommender Systems | Content-Based Filtering |
| 439 | Recommender Systems | Hybrid Recommender Systems |
| 440 | Recommender Systems | Knowledge-Based Recommender Systems |
| 441 | Recommender Systems | Reinforcement Learning for Recommender Systems |
| 448 | Model Deployment | Containerization |
| 449 | Model Deployment | Serverless Computing |
| 450 | Model Deployment | Model Monitoring |
| 451 | Causal Inference | Pearl's Causal Model |
| 452 | Causal Inference | Propensity Score Matching |
| 453 | Causal Inference | Instrumental Variable Analysis |
| 454 | Causal Inference | Difference-in-Differences (DiD) |
| 455 | Causal Inference | Regression Discontinuity Design (RDD) |
| 456 | Automated ML | AutoML |
| 457 | Automated ML | Neural Architecture Search (NAS) |
| 458 | Automated ML | Model Stacking |
| 459 | Automated ML | Feature Selection |
| 460 | Automated ML | Hyperparameter Optimization |
| 461 | Model Explainability | LIME (Local Interpretable Model-Agnostic Explanations) |
| 462 | Model Explainability | SHAP (SHapley Additive exPlanations) |
| 463 | Model Explainability | PDP (Partial Dependence Plots) |
| 464 | Model Explainability | ELI5 (Explain Like I'm 5) |
| 465 | Model Explainability | Anchors |
| 466 | Geospatial Analysis | GIS (Geographic Information System) |
| 467 | Geospatial Analysis | Spatial Data Visualization |
| 468 | Geospatial Analysis | Geoanalytics |
| 469 | Geospatial Analysis | GeoStatistics |
| 470 | Geospatial Analysis | Spatial Regression Analysis |
| 471 | Time Series Forecasting | Exponential Smoothing Methods |
| 472 | Time Series Forecasting | ARIMA (Autoregressive Integrated Moving Average) |
| 473 | Time Series Forecasting | Prophet (Time Series Forecasting) |
| 474 | Time Series Forecasting | Neural Networks for Time Series Forecasting |
| 475 | Time Series Forecasting | Ensemble Methods for Time Series Forecasting |
| 476 | Data Augmentation | Flip and Rotation |
| 477 | Data Augmentation | Random Crop and Resize |
| 478 | Data Augmentation | Noise Injection |
| 479 | Data Augmentation | Augmentation with Generative Models |
| 480 | Data Augmentation | Mixup and CutMix |
| 481 | Knowledge Graph | Knowledge Graph Representation Learning |
| 482 | Knowledge Graph | Knowledge Graph Embeddings |
| 483 | Knowledge Graph | Knowledge Graph Alignment |
| 484 | Knowledge Graph | Knowledge Graph Completion |
| 485 | Knowledge Graph | Querying and Reasoning over Knowledge Graphs |
| 486 | Distributed Computing | Apache Hadoop |
| 487 | Distributed Computing | Apache Spark |
| 488 | Distributed Computing | Distributed Deep Learning |
| 489 | Distributed Computing | Distributed Data Processing |
| 490 | Distributed Computing | Distributed File Systems |
| 491 | Adversarial Defense | Adversarial Training |
| 492 | Adversarial Defense | Defensive Distillation |
| 493 | Adversarial Defense | Randomized Smoothing |
| 494 | Adversarial Defense | Feature Squeezing |
| 495 | Adversarial Defense | Ensemble Adversarial Training |
| 496 | Natural Language Understanding | Entity Recognition |
| 497 | Natural Language Understanding | Dependency Parsing |
| 498 | Natural Language Understanding | Named Entity Recognition (NER) |
| 499 | Natural Language Understanding | Sentiment Analysis |
| 500 | Natural Language Understanding | Coreference Resolution |
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PyVerse is released under the MIT License.