advanced_ml_ai_frameworks.py

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# Advanced Machine Learning and AI Frameworks Dataset

# Deep Learning Frameworks
tensorflow_components = [
    "tf.keras", "tf.data", "tf.nn", "tf.losses", "tf.metrics", "tf.optimizers",
    "tf.layers", "tf.estimator", "tf.feature_column", "tf.summary", "tf.saved_model",
    "tf.lite", "tf.js", "tf.serving", "tf.probability", "tf.agents", "tf.federated"
]

pytorch_components = [
    "torch.nn", "torch.optim", "torch.utils.data", "torch.autograd", "torch.cuda",
    "torchvision", "torchaudio", "torchtext", "torch.distributed", "torch.jit",
    "torch.quantization", "torch.onnx", "torch.fx", "torch.profiler"
]

# Natural Language Processing Libraries
nlp_frameworks = {
    "transformers": ["BERT", "GPT", "T5", "RoBERTa", "DistilBERT", "ALBERT", "XLNet"],
    "spacy": ["tokenization", "pos_tagging", "ner", "dependency_parsing", "similarity"],
    "nltk": ["corpus", "tokenize", "stem", "tag", "parse", "sentiment", "wordnet"],
    "gensim": ["word2vec", "doc2vec", "fasttext", "lda", "lsi", "tfidf"],
    "allennlp": ["text_classification", "named_entity_recognition", "reading_comprehension"],
    "flair": ["sequence_labeling", "text_classification", "word_embeddings"]
}

# Computer Vision Libraries
cv_frameworks = {
    "opencv": ["image_processing", "video_processing", "feature_detection", "object_tracking"],
    "pillow": ["image_manipulation", "filters", "transformations", "format_conversion"],
    "scikit_image": ["segmentation", "feature_extraction", "restoration", "morphology"],
    "albumentations": ["data_augmentation", "transforms", "bounding_boxes", "keypoints"],
    "detectron2": ["object_detection", "instance_segmentation", "panoptic_segmentation"],
    "mmdetection": ["faster_rcnn", "mask_rcnn", "yolo", "ssd", "retinanet"]
}

# Data Science and Analytics
data_science_stack = {
    "pandas": ["dataframe", "series", "groupby", "merge", "pivot", "time_series"],
    "numpy": ["arrays", "linear_algebra", "statistics", "random", "fft"],
    "scipy": ["optimization", "integration", "interpolation", "signal_processing"],
    "statsmodels": ["regression", "time_series", "hypothesis_testing", "anova"],
    "scikit_learn": ["classification", "regression", "clustering", "preprocessing"],
    "xgboost": ["gradient_boosting", "feature_importance", "cross_validation"],
    "lightgbm": ["fast_training", "memory_efficiency", "categorical_features"],
    "catboost": ["categorical_encoding", "overfitting_detection", "gpu_support"]
}

# MLOps and Model Deployment
mlops_tools = {
    "mlflow": ["experiment_tracking", "model_registry", "deployment", "projects"],
    "kubeflow": ["pipelines", "katib", "kfserving", "notebooks"],
    "airflow": ["workflow_orchestration", "dag", "operators", "sensors"],
    "dvc": ["data_versioning", "pipeline_management", "experiment_tracking"],
    "wandb": ["experiment_tracking", "hyperparameter_tuning", "model_monitoring"],
    "neptune": ["metadata_store", "experiment_management", "model_registry"],
    "tensorboard": ["visualization", "profiling", "debugging", "hyperparameters"]
}

# Distributed Computing Frameworks
distributed_ml = {
    "ray": ["distributed_training", "hyperparameter_tuning", "reinforcement_learning"],
    "dask": ["parallel_computing", "delayed_computation", "distributed_arrays"],
    "spark": ["mllib", "structured_streaming", "graphx", "sql"],
    "horovod": ["distributed_deep_learning", "tensorflow", "pytorch", "mxnet"],
    "deepspeed": ["model_parallelism", "gradient_compression", "mixed_precision"]
}

# Reinforcement Learning
rl_frameworks = {
    "stable_baselines3": ["ppo", "a2c", "sac", "td3", "ddpg", "dqn"],
    "ray_rllib": ["multi_agent", "distributed_training", "custom_environments"],
    "openai_gym": ["environments", "wrappers", "monitoring", "vector_envs"],
    "tensorflow_agents": ["dqn", "ddpg", "td3", "sac", "ppo", "behavioral_cloning"],
    "acme": ["distributed_rl", "research_framework", "continuous_control"]
}

# AutoML Frameworks
automl_tools = {
    "auto_sklearn": ["automated_feature_selection", "hyperparameter_optimization"],
    "tpot": ["genetic_programming", "pipeline_optimization", "feature_engineering"],
    "h2o": ["automl", "explainable_ai", "model_interpretability"],
    "autokeras": ["neural_architecture_search", "automated_deep_learning"],
    "flaml": ["efficient_hyperparameter_tuning", "automated_feature_engineering"],
    "optuna": ["hyperparameter_optimization", "pruning", "visualization"]
}

# Model Interpretability
explainability_tools = {
    "shap": ["shapley_values", "feature_importance", "model_explanation"],
    "lime": ["local_interpretability", "tabular_data", "text_data", "image_data"],
    "eli5": ["permutation_importance", "feature_names", "decision_trees"],
    "captum": ["attribution_algorithms", "pytorch_integration", "visualization"],
    "alibi": ["counterfactual_explanations", "adversarial_detection", "anchor_explanations"]
}

# Time Series Analysis
time_series_libraries = {
    "prophet": ["forecasting", "trend_analysis", "seasonality", "holiday_effects"],
    "statsforecast": ["statistical_models", "cross_validation", "feature_engineering"],
    "darts": ["modern_forecasting", "multiple_series", "probabilistic_forecasting"],
    "sktime": ["time_series_classification", "regression", "clustering", "annotation"],
    "tsfresh": ["feature_extraction", "feature_selection", "time_series_characterization"],
    "pyflux": ["bayesian_inference", "state_space_models", "gas_models"]
}

# Feature Engineering
feature_engineering_tools = {
    "featuretools": ["automated_feature_engineering", "deep_feature_synthesis"],
    "category_encoders": ["target_encoding", "binary_encoding", "hashing"],
    "feature_engine": ["variable_transformation", "discretization", "outlier_handling"],
    "boruta": ["feature_selection", "random_forest", "statistical_significance"],
    "optbinning": ["optimal_binning", "scorecard_development", "risk_modeling"]
}

# Model Monitoring and Observability
monitoring_tools = {
    "evidently": ["data_drift", "model_performance", "target_drift"],
    "whylogs": ["data_logging", "statistical_profiling", "anomaly_detection"],
    "great_expectations": ["data_validation", "data_documentation", "data_profiling"],
    "deepchecks": ["model_validation", "data_integrity", "distribution_checks"],
    "fiddler": ["model_monitoring", "explainability", "fairness_analysis"]
}

# Edge AI and Mobile Deployment
edge_ai_tools = {
    "tensorflow_lite": ["mobile_deployment", "quantization", "optimization"],
    "onnx": ["model_interoperability", "runtime_optimization", "hardware_acceleration"],
    "tensorrt": ["nvidia_optimization", "inference_acceleration", "precision_reduction"],
    "openvino": ["intel_optimization", "model_compression", "deployment_tools"],
    "coreml": ["apple_deployment", "on_device_inference", "model_conversion"],
    "pytorch_mobile": ["android_deployment", "ios_deployment", "model_optimization"]
}

# Quantum Machine Learning
quantum_ml = {
    "qiskit": ["quantum_circuits", "quantum_algorithms", "quantum_simulators"],
    "cirq": ["quantum_computing", "noise_simulation", "variational_algorithms"],
    "pennylane": ["quantum_machine_learning", "hybrid_algorithms", "automatic_differentiation"],
    "tensorflow_quantum": ["quantum_neural_networks", "variational_circuits"],
    "forest": ["quantum_cloud_services", "pyquil", "quilc"]
}

# Federated Learning
federated_learning = {
    "tensorflow_federated": ["federated_averaging", "differential_privacy"],
    "pysyft": ["privacy_preserving_ml", "secure_multi_party_computation"],
    "flower": ["federated_learning_framework", "cross_platform_support"],
    "fate": ["industrial_federated_learning", "secure_computation"],
    "fedml": ["research_platform", "mobile_federated_learning"]
}

# Graph Neural Networks
graph_ml = {
    "pytorch_geometric": ["graph_convolutions", "graph_pooling", "graph_classification"],
    "dgl": ["deep_graph_library", "heterogeneous_graphs", "distributed_training"],
    "spektral": ["keras_integration", "graph_attention", "graph_autoencoders"],
    "networkx": ["graph_analysis", "algorithms", "visualization"],
    "graph_nets": ["deepmind_framework", "message_passing", "graph_networks"]
}

# Audio Processing
audio_ml = {
    "librosa": ["audio_analysis", "feature_extraction", "spectral_analysis"],
    "torchaudio": ["pytorch_audio", "transforms", "datasets"],
    "pyaudio": ["audio_io", "real_time_processing", "streaming"],
    "speechrecognition": ["speech_to_text", "multiple_engines", "offline_recognition"],
    "pydub": ["audio_manipulation", "format_conversion", "effects"]
}

# Medical AI
medical_ai_tools = {
    "monai": ["medical_imaging", "pytorch_integration", "transforms"],
    "nibabel": ["neuroimaging", "file_formats", "spatial_images"],
    "pydicom": ["dicom_processing", "medical_image_io", "metadata_extraction"],
    "simpleitk": ["image_registration", "segmentation", "filtering"],
    "medpy": ["medical_image_processing", "io_functions", "utilities"]
}

# Robotics and Control
robotics_ml = {
    "ros": ["robot_operating_system", "nodes", "topics", "services"],
    "pybullet": ["physics_simulation", "robotics", "machine_learning"],
    "mujoco": ["physics_engine", "model_predictive_control", "simulation"],
    "drake": ["manipulation", "planning", "control_systems"],
    "pinocchio": ["rigid_body_dynamics", "computational_geometry", "algorithms"]
}

# Optimization Libraries
optimization_tools = {
    "optuna": ["hyperparameter_optimization", "pruning", "distributed_optimization"],
    "hyperopt": ["bayesian_optimization", "tree_parzen_estimators", "random_search"],
    "scikit_optimize": ["gaussian_processes", "acquisition_functions", "space_definition"],
    "nevergrad": ["derivative_free_optimization", "evolutionary_algorithms"],
    "ray_tune": ["distributed_hyperparameter_tuning", "early_stopping", "scheduling"]
}

# Privacy-Preserving ML
privacy_ml = {
    "opacus": ["differential_privacy", "pytorch_integration", "privacy_accounting"],
    "tensorflow_privacy": ["dp_sgd", "privacy_analysis", "membership_inference"],
    "diffprivlib": ["differential_privacy_library", "machine_learning_algorithms"],
    "crypten": ["secure_multiparty_computation", "pytorch_extension"],
    "syft": ["federated_learning", "differential_privacy", "encrypted_computation"]
}

# Cloud ML Platforms
cloud_ml_services = {
    "aws_sagemaker": ["training", "deployment", "autopilot", "feature_store"],
    "google_cloud_ai": ["vertex_ai", "automl", "ai_platform", "bigquery_ml"],
    "azure_ml": ["automated_ml", "designer", "compute_instances", "model_registry"],
    "databricks": ["collaborative_notebooks", "mlflow", "delta_lake", "automl"],
    "paperspace": ["gradient", "notebooks", "deployments", "workflows"]
}

# Model Compression and Acceleration
model_optimization = {
    "pruning": ["magnitude_pruning", "structured_pruning", "lottery_ticket_hypothesis"],
    "quantization": ["post_training_quantization", "quantization_aware_training"],
    "distillation": ["knowledge_distillation", "teacher_student_models"],
    "neural_architecture_search": ["differentiable_nas", "evolutionary_nas"],
    "model_compression": ["huffman_coding", "weight_sharing", "low_rank_approximation"]
}

# Benchmark Datasets
benchmark_datasets = {
    "computer_vision": ["imagenet", "coco", "cifar", "mnist", "open_images"],
    "natural_language": ["glue", "superglue", "squad", "wmt", "common_crawl"],
    "speech": ["librispeech", "common_voice", "voxceleb", "speech_commands"],
    "reinforcement_learning": ["atari", "mujoco", "unity_ml_agents", "procgen"],
    "recommendation": ["movielens", "amazon_reviews", "yelp", "lastfm"],
    "time_series": ["m4_competition", "electricity", "traffic", "exchange_rate"]
}

# Evaluation Metrics
ml_metrics = {
    "classification": ["accuracy", "precision", "recall", "f1_score", "auc_roc", "log_loss"],
    "regression": ["mse", "mae", "rmse", "r2_score", "mape", "huber_loss"],
    "ranking": ["ndcg", "map", "mrr", "precision_at_k", "recall_at_k"],
    "clustering": ["silhouette_score", "davies_bouldin", "calinski_harabasz", "ari"],
    "object_detection": ["map", "iou", "precision", "recall", "fps"],
    "nlp": ["bleu", "rouge", "meteor", "perplexity", "bert_score"]
}

# Research Areas and Trends
ml_research_areas = {
    "foundation_models": ["large_language_models", "vision_transformers", "multimodal_models"],
    "few_shot_learning": ["meta_learning", "prototype_networks", "model_agnostic_meta_learning"],
    "continual_learning": ["catastrophic_forgetting", "elastic_weight_consolidation", "replay_methods"],
    "adversarial_ml": ["adversarial_examples", "robust_optimization", "certified_defense"],
    "causality": ["causal_inference", "do_calculus", "causal_discovery"],
    "fairness": ["algorithmic_bias", "demographic_parity", "equalized_odds"],
    "uncertainty_quantification": ["bayesian_deep_learning", "monte_carlo_dropout", "ensemble_methods"]
}

# Implementation Patterns
ml_implementation_patterns = {
    "data_pipeline": "Extract -> Transform -> Load -> Validate -> Feature Engineering",
    "model_lifecycle": "Research -> Development -> Training -> Validation -> Deployment -> Monitoring",
    "experiment_tracking": "Version Control -> Configuration -> Metrics -> Artifacts -> Reproducibility",
    "feature_store": "Ingestion -> Storage -> Serving -> Monitoring -> Lineage",
    "model_serving": "Preprocessing -> Inference -> Postprocessing -> Monitoring -> Feedback"
}

# Code Templates for Common ML Tasks
ml_code_templates = {
    "train_test_split": """
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
""",
    "cross_validation": """
from sklearn.model_selection import cross_val_score
scores = cross_val_score(model, X, y, cv=5, scoring='accuracy')
""",
    "grid_search": """
from sklearn.model_selection import GridSearchCV
grid_search = GridSearchCV(model, param_grid, cv=5, scoring='accuracy')
grid_search.fit(X_train, y_train)
""",
    "pipeline": """
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('classifier', LogisticRegression())
])
""",
    "feature_selection": """
from sklearn.feature_selection import SelectKBest, f_classif
selector = SelectKBest(score_func=f_classif, k=10)
X_selected = selector.fit_transform(X, y)
"""
}

# Performance Optimization Tips
optimization_tips = {
    "data_loading": "Use data generators and prefetching for large datasets",
    "model_training": "Implement gradient accumulation for large batch sizes",
    "inference": "Use batch processing and model quantization for speed",
    "memory": "Implement gradient checkpointing to reduce memory usage",
    "distributed": "Use data parallelism and model parallelism appropriately",
    "profiling": "Profile your code to identify bottlenecks and optimize accordingly"
}

# Common Pitfalls and Solutions
ml_pitfalls = {
    "data_leakage": "Ensure no future information leaks into training data",
    "overfitting": "Use regularization, dropout, and proper validation",
    "underfitting": "Increase model complexity or reduce regularization",
    "bias_variance": "Balance model complexity with generalization ability",
    "evaluation": "Use appropriate metrics and validation strategies",
    "reproducibility": "Set random seeds and document environments",
    "scalability": "Design for production scale from the beginning"
}

# Latest Developments and Trends
current_trends = {
    "large_language_models": ["gpt", "bert", "t5", "palm", "claude", "llama"],
    "multimodal_ai": ["clip", "dall-e", "flamingo", "blip", "align"],
    "efficient_architectures": ["mobilenet", "efficientnet", "vision_transformer"],
    "self_supervised_learning": ["contrastive_learning", "masked_language_modeling"],
    "neural_scaling_laws": ["parameter_scaling", "compute_scaling", "data_scaling"],
    "ai_safety": ["alignment", "robustness", "interpretability", "value_learning"]
}

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advanced_ml_ai_frameworks.pyPY

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