🤖 AI/ML Patterns with TuskLang Java

"We don't bow to any king" - AI Edition

TuskLang Java enables sophisticated AI/ML applications with built-in support for machine learning models, neural networks, data preprocessing, and intelligent decision-making. Build intelligent applications that learn, adapt, and make predictions.

🎯 AI/ML Architecture Overview

Machine Learning Configuration

import org.tusklang.java.TuskLang;
import org.tusklang.java.config.TuskConfig;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class AIMLApplication {
    
    @Bean
    public TuskConfig tuskConfig() {
        TuskLang parser = new TuskLang();
        return parser.parseFile("ai-ml.tsk", TuskConfig.class);
    }
    
    public static void main(String[] args) {
        SpringApplication.run(AIMLApplication.class, args);
    }
}
// AI/ML configuration
@TuskConfig
public class AIMLConfig {
    private String applicationName;
    private String version;
    private ModelConfig model;
    private DataConfig data;
    private TrainingConfig training;
    private InferenceConfig inference;
    private MonitoringConfig monitoring;
    
    // Getters and setters
    public String getApplicationName() { return applicationName; }
    public void setApplicationName(String applicationName) { this.applicationName = applicationName; }
    
    public String getVersion() { return version; }
    public void setVersion(String version) { this.version = version; }
    
    public ModelConfig getModel() { return model; }
    public void setModel(ModelConfig model) { this.model = model; }
    
    public DataConfig getData() { return data; }
    public void setData(DataConfig data) { this.data = data; }
    
    public TrainingConfig getTraining() { return training; }
    public void setTraining(TrainingConfig training) { this.training = training; }
    
    public InferenceConfig getInference() { return inference; }
    public void setInference(InferenceConfig inference) { this.inference = inference; }
    
    public MonitoringConfig getMonitoring() { return monitoring; }
    public void setMonitoring(MonitoringConfig monitoring) { this.monitoring = monitoring; }
}
@TuskConfig
public class ModelConfig {
    private String type;
    private String framework;
    private String modelPath;
    private String modelVersion;
    private HyperparametersConfig hyperparameters;
    private ArchitectureConfig architecture;
    
    // Getters and setters
    public String getType() { return type; }
    public void setType(String type) { this.type = type; }
    
    public String getFramework() { return framework; }
    public void setFramework(String framework) { this.framework = framework; }
    
    public String getModelPath() { return modelPath; }
    public void setModelPath(String modelPath) { this.modelPath = modelPath; }
    
    public String getModelVersion() { return modelVersion; }
    public void setModelVersion(String modelVersion) { this.modelVersion = modelVersion; }
    
    public HyperparametersConfig getHyperparameters() { return hyperparameters; }
    public void setHyperparameters(HyperparametersConfig hyperparameters) { this.hyperparameters = hyperparameters; }
    
    public ArchitectureConfig getArchitecture() { return architecture; }
    public void setArchitecture(ArchitectureConfig architecture) { this.architecture = architecture; }
}
@TuskConfig
public class HyperparametersConfig {
    private double learningRate;
    private int batchSize;
    private int epochs;
    private double dropoutRate;
    private String optimizer;
    private String lossFunction;
    private Map<String, Object> customParams;
    
    // Getters and setters
    public double getLearningRate() { return learningRate; }
    public void setLearningRate(double learningRate) { this.learningRate = learningRate; }
    
    public int getBatchSize() { return batchSize; }
    public void setBatchSize(int batchSize) { this.batchSize = batchSize; }
    
    public int getEpochs() { return epochs; }
    public void setEpochs(int epochs) { this.epochs = epochs; }
    
    public double getDropoutRate() { return dropoutRate; }
    public void setDropoutRate(double dropoutRate) { this.dropoutRate = dropoutRate; }
    
    public String getOptimizer() { return optimizer; }
    public void setOptimizer(String optimizer) { this.optimizer = optimizer; }
    
    public String getLossFunction() { return lossFunction; }
    public void setLossFunction(String lossFunction) { this.lossFunction = lossFunction; }
    
    public Map<String, Object> getCustomParams() { return customParams; }
    public void setCustomParams(Map<String, Object> customParams) { this.customParams = customParams; }
}
@TuskConfig
public class ArchitectureConfig {
    private List<Integer> layers;
    private List<String> activations;
    private String inputShape;
    private String outputShape;
    private boolean batchNormalization;
    private boolean regularization;
    
    // Getters and setters
    public List<Integer> getLayers() { return layers; }
    public void setLayers(List<Integer> layers) { this.layers = layers; }
    
    public List<String> getActivations() { return activations; }
    public void setActivations(List<String> activations) { this.activations = activations; }
    
    public String getInputShape() { return inputShape; }
    public void setInputShape(String inputShape) { this.inputShape = inputShape; }
    
    public String getOutputShape() { return outputShape; }
    public void setOutputShape(String outputShape) { this.outputShape = outputShape; }
    
    public boolean isBatchNormalization() { return batchNormalization; }
    public void setBatchNormalization(boolean batchNormalization) { this.batchNormalization = batchNormalization; }
    
    public boolean isRegularization() { return regularization; }
    public void setRegularization(boolean regularization) { this.regularization = regularization; }
}
@TuskConfig
public class DataConfig {
    private String source;
    private String format;
    private PreprocessingConfig preprocessing;
    private AugmentationConfig augmentation;
    private ValidationConfig validation;
    
    // Getters and setters
    public String getSource() { return source; }
    public void setSource(String source) { this.source = source; }
    
    public String getFormat() { return format; }
    public void setFormat(String format) { this.format = format; }
    
    public PreprocessingConfig getPreprocessing() { return preprocessing; }
    public void setPreprocessing(PreprocessingConfig preprocessing) { this.preprocessing = preprocessing; }
    
    public AugmentationConfig getAugmentation() { return augmentation; }
    public void setAugmentation(AugmentationConfig augmentation) { this.augmentation = augmentation; }
    
    public ValidationConfig getValidation() { return validation; }
    public void setValidation(ValidationConfig validation) { this.validation = validation; }
}
@TuskConfig
public class PreprocessingConfig {
    private boolean normalization;
    private boolean standardization;
    private boolean scaling;
    private String scalingMethod;
    private boolean featureSelection;
    private List<String> selectedFeatures;
    private boolean outlierRemoval;
    
    // Getters and setters
    public boolean isNormalization() { return normalization; }
    public void setNormalization(boolean normalization) { this.normalization = normalization; }
    
    public boolean isStandardization() { return standardization; }
    public void setStandardization(boolean standardization) { this.standardization = standardization; }
    
    public boolean isScaling() { return scaling; }
    public void setScaling(boolean scaling) { this.scaling = scaling; }
    
    public String getScalingMethod() { return scalingMethod; }
    public void setScalingMethod(String scalingMethod) { this.scalingMethod = scalingMethod; }
    
    public boolean isFeatureSelection() { return featureSelection; }
    public void setFeatureSelection(boolean featureSelection) { this.featureSelection = featureSelection; }
    
    public List<String> getSelectedFeatures() { return selectedFeatures; }
    public void setSelectedFeatures(List<String> selectedFeatures) { this.selectedFeatures = selectedFeatures; }
    
    public boolean isOutlierRemoval() { return outlierRemoval; }
    public void setOutlierRemoval(boolean outlierRemoval) { this.outlierRemoval = outlierRemoval; }
}
@TuskConfig
public class AugmentationConfig {
    private boolean enabled;
    private double rotationRange;
    private double widthShiftRange;
    private double heightShiftRange;
    private double zoomRange;
    private double horizontalFlip;
    private double verticalFlip;
    
    // Getters and setters
    public boolean isEnabled() { return enabled; }
    public void setEnabled(boolean enabled) { this.enabled = enabled; }
    
    public double getRotationRange() { return rotationRange; }
    public void setRotationRange(double rotationRange) { this.rotationRange = rotationRange; }
    
    public double getWidthShiftRange() { return widthShiftRange; }
    public void setWidthShiftRange(double widthShiftRange) { this.widthShiftRange = widthShiftRange; }
    
    public double getHeightShiftRange() { return heightShiftRange; }
    public void setHeightShiftRange(double heightShiftRange) { this.heightShiftRange = heightShiftRange; }
    
    public double getZoomRange() { return zoomRange; }
    public void setZoomRange(double zoomRange) { this.zoomRange = zoomRange; }
    
    public double getHorizontalFlip() { return horizontalFlip; }
    public void setHorizontalFlip(double horizontalFlip) { this.horizontalFlip = horizontalFlip; }
    
    public double getVerticalFlip() { return verticalFlip; }
    public void setVerticalFlip(double verticalFlip) { this.verticalFlip = verticalFlip; }
}
@TuskConfig
public class ValidationConfig {
    private double testSize;
    private double validationSize;
    private String splitMethod;
    private int crossValidationFolds;
    private boolean stratified;
    
    // Getters and setters
    public double getTestSize() { return testSize; }
    public void setTestSize(double testSize) { this.testSize = testSize; }
    
    public double getValidationSize() { return validationSize; }
    public void setValidationSize(double validationSize) { this.validationSize = validationSize; }
    
    public String getSplitMethod() { return splitMethod; }
    public void setSplitMethod(String splitMethod) { this.splitMethod = splitMethod; }
    
    public int getCrossValidationFolds() { return crossValidationFolds; }
    public void setCrossValidationFolds(int crossValidationFolds) { this.crossValidationFolds = crossValidationFolds; }
    
    public boolean isStratified() { return stratified; }
    public void setStratified(boolean stratified) { this.stratified = stratified; }
}
@TuskConfig
public class TrainingConfig {
    private boolean enabled;
    private String mode;
    private String gpuConfig;
    private CheckpointConfig checkpoint;
    private EarlyStoppingConfig earlyStopping;
    private CallbackConfig callbacks;
    
    // Getters and setters
    public boolean isEnabled() { return enabled; }
    public void setEnabled(boolean enabled) { this.enabled = enabled; }
    
    public String getMode() { return mode; }
    public void setMode(String mode) { this.mode = mode; }
    
    public String getGpuConfig() { return gpuConfig; }
    public void setGpuConfig(String gpuConfig) { this.gpuConfig = gpuConfig; }
    
    public CheckpointConfig getCheckpoint() { return checkpoint; }
    public void setCheckpoint(CheckpointConfig checkpoint) { this.checkpoint = checkpoint; }
    
    public EarlyStoppingConfig getEarlyStopping() { return earlyStopping; }
    public void setEarlyStopping(EarlyStoppingConfig earlyStopping) { this.earlyStopping = earlyStopping; }
    
    public CallbackConfig getCallbacks() { return callbacks; }
    public void setCallbacks(CallbackConfig callbacks) { this.callbacks = callbacks; }
}
@TuskConfig
public class CheckpointConfig {
    private boolean enabled;
    private String path;
    private int saveFrequency;
    private boolean saveBestOnly;
    private String monitor;
    
    // Getters and setters
    public boolean isEnabled() { return enabled; }
    public void setEnabled(boolean enabled) { this.enabled = enabled; }
    
    public String getPath() { return path; }
    public void setPath(String path) { this.path = path; }
    
    public int getSaveFrequency() { return saveFrequency; }
    public void setSaveFrequency(int saveFrequency) { this.saveFrequency = saveFrequency; }
    
    public boolean isSaveBestOnly() { return saveBestOnly; }
    public void setSaveBestOnly(boolean saveBestOnly) { this.saveBestOnly = saveBestOnly; }
    
    public String getMonitor() { return monitor; }
    public void setMonitor(String monitor) { this.monitor = monitor; }
}
@TuskConfig
public class EarlyStoppingConfig {
    private boolean enabled;
    private int patience;
    private double minDelta;
    private String monitor;
    private String mode;
    
    // Getters and setters
    public boolean isEnabled() { return enabled; }
    public void setEnabled(boolean enabled) { this.enabled = enabled; }
    
    public int getPatience() { return patience; }
    public void setPatience(int patience) { this.patience = patience; }
    
    public double getMinDelta() { return minDelta; }
    public void setMinDelta(double minDelta) { this.minDelta = minDelta; }
    
    public String getMonitor() { return monitor; }
    public void setMonitor(String monitor) { this.monitor = monitor; }
    
    public String getMode() { return mode; }
    public void setMode(String mode) { this.mode = mode; }
}
@TuskConfig
public class CallbackConfig {
    private boolean tensorboard;
    private boolean csvLogger;
    private boolean reduceLROnPlateau;
    private Map<String, Object> customCallbacks;
    
    // Getters and setters
    public boolean isTensorboard() { return tensorboard; }
    public void setTensorboard(boolean tensorboard) { this.tensorboard = tensorboard; }
    
    public boolean isCsvLogger() { return csvLogger; }
    public void setCsvLogger(boolean csvLogger) { this.csvLogger = csvLogger; }
    
    public boolean isReduceLROnPlateau() { return reduceLROnPlateau; }
    public void setReduceLROnPlateau(boolean reduceLROnPlateau) { this.reduceLROnPlateau = reduceLROnPlateau; }
    
    public Map<String, Object> getCustomCallbacks() { return customCallbacks; }
    public void setCustomCallbacks(Map<String, Object> customCallbacks) { this.customCallbacks = customCallbacks; }
}
@TuskConfig
public class InferenceConfig {
    private boolean enabled;
    private String mode;
    private int batchSize;
    private boolean caching;
    private String cachePath;
    private PerformanceConfig performance;
    
    // Getters and setters
    public boolean isEnabled() { return enabled; }
    public void setEnabled(boolean enabled) { this.enabled = enabled; }
    
    public String getMode() { return mode; }
    public void setMode(String mode) { this.mode = mode; }
    
    public int getBatchSize() { return batchSize; }
    public void setBatchSize(int batchSize) { this.batchSize = batchSize; }
    
    public boolean isCaching() { return caching; }
    public void setCaching(boolean caching) { this.caching = caching; }
    
    public String getCachePath() { return cachePath; }
    public void setCachePath(String cachePath) { this.cachePath = cachePath; }
    
    public PerformanceConfig getPerformance() { return performance; }
    public void setPerformance(PerformanceConfig performance) { this.performance = performance; }
}
@TuskConfig
public class PerformanceConfig {
    private boolean optimization;
    private String optimizationLevel;
    private boolean quantization;
    private String quantizationType;
    private boolean pruning;
    private double pruningRate;
    
    // Getters and setters
    public boolean isOptimization() { return optimization; }
    public void setOptimization(boolean optimization) { this.optimization = optimization; }
    
    public String getOptimizationLevel() { return optimizationLevel; }
    public void setOptimizationLevel(String optimizationLevel) { this.optimizationLevel = optimizationLevel; }
    
    public boolean isQuantization() { return quantization; }
    public void setQuantization(boolean quantization) { this.quantization = quantization; }
    
    public String getQuantizationType() { return quantizationType; }
    public void setQuantizationType(String quantizationType) { this.quantizationType = quantizationType; }
    
    public boolean isPruning() { return pruning; }
    public void setPruning(boolean pruning) { this.pruning = pruning; }
    
    public double getPruningRate() { return pruningRate; }
    public void setPruningRate(double pruningRate) { this.pruningRate = pruningRate; }
}
@TuskConfig
public class MonitoringConfig {
    private String prometheusEndpoint;
    private boolean enabled;
    private Map<String, String> labels;
    private int scrapeInterval;
    private AlertingConfig alerting;
    
    // Getters and setters
    public String getPrometheusEndpoint() { return prometheusEndpoint; }
    public void setPrometheusEndpoint(String prometheusEndpoint) { this.prometheusEndpoint = prometheusEndpoint; }
    
    public boolean isEnabled() { return enabled; }
    public void setEnabled(boolean enabled) { this.enabled = enabled; }
    
    public Map<String, String> getLabels() { return labels; }
    public void setLabels(Map<String, String> labels) { this.labels = labels; }
    
    public int getScrapeInterval() { return scrapeInterval; }
    public void setScrapeInterval(int scrapeInterval) { this.scrapeInterval = scrapeInterval; }
    
    public AlertingConfig getAlerting() { return alerting; }
    public void setAlerting(AlertingConfig alerting) { this.alerting = alerting; }
}
@TuskConfig
public class AlertingConfig {
    private String slackWebhook;
    private String emailEndpoint;
    private Map<String, AlertRule> rules;
    
    // Getters and setters
    public String getSlackWebhook() { return slackWebhook; }
    public void setSlackWebhook(String slackWebhook) { this.slackWebhook = slackWebhook; }
    
    public String getEmailEndpoint() { return emailEndpoint; }
    public void setEmailEndpoint(String emailEndpoint) { this.emailEndpoint = emailEndpoint; }
    
    public Map<String, AlertRule> getRules() { return rules; }
    public void setRules(Map<String, AlertRule> rules) { this.rules = rules; }
}
@TuskConfig
public class AlertRule {
    private String condition;
    private String threshold;
    private String duration;
    private List<String> channels;
    private String severity;
    
    // Getters and setters
    public String getCondition() { return condition; }
    public void setCondition(String condition) { this.condition = condition; }
    
    public String getThreshold() { return threshold; }
    public void setThreshold(String threshold) { this.threshold = threshold; }
    
    public String getDuration() { return duration; }
    public void setDuration(String duration) { this.duration = duration; }
    
    public List<String> getChannels() { return channels; }
    public void setChannels(List<String> channels) { this.channels = channels; }
    
    public String getSeverity() { return severity; }
    public void setSeverity(String severity) { this.severity = severity; }
}

🏗️ AI/ML TuskLang Configuration

ai-ml.tsk

AI/ML Configuration
[application]
name: "user-prediction-service"
version: "2.1.0"
environment: @env("ENVIRONMENT", "production")
[model]
type: "neural_network"
framework: "tensorflow"
model_path: @env("MODEL_PATH", "/models/user-prediction")
model_version: "2.1.0"
[hyperparameters]
learning_rate: @learn("optimal_learning_rate", "0.001")
batch_size: @learn("optimal_batch_size", "32")
epochs: @learn("optimal_epochs", "100")
dropout_rate: @learn("optimal_dropout_rate", "0.2")
optimizer: "adam"
loss_function: "binary_crossentropy"
custom_params {
    "momentum": 0.9
    "weight_decay": 0.0001
    "epsilon": 1e-8
}
[architecture]
layers: [128, 64, 32, 16, 1]
activations: ["relu", "relu", "relu", "relu", "sigmoid"]
input_shape: "(10,)"
output_shape: "(1,)"
batch_normalization: true
regularization: true
[data]
source: @env("DATA_SOURCE", "postgresql://localhost:5432/user_data")
format: "csv"
[preprocessing]
normalization: true
standardization: false
scaling: true
scaling_method: "min_max"
feature_selection: true
selected_features: [
    "age",
    "income",
    "education_level",
    "location_score",
    "activity_level",
    "purchase_history",
    "session_duration",
    "click_rate",
    "conversion_rate",
    "loyalty_score"
]
outlier_removal: true
[augmentation]
enabled: true
rotation_range: 0.1
width_shift_range: 0.1
height_shift_range: 0.1
zoom_range: 0.1
horizontal_flip: 0.5
vertical_flip: 0.0
[validation]
test_size: 0.2
validation_size: 0.2
split_method: "random"
cross_validation_folds: 5
stratified: true
[training]
enabled: true
mode: "gpu"
gpu_config: @env("GPU_CONFIG", "0")
[checkpoint]
enabled: true
path: "/models/checkpoints"
save_frequency: 10
save_best_only: true
monitor: "val_loss"
[early_stopping]
enabled: true
patience: 15
min_delta: 0.001
monitor: "val_loss"
mode: "min"
[callbacks]
tensorboard: true
csv_logger: true
reduce_lr_on_plateau: true
custom_callbacks {
    "custom_metric": "f1_score"
    "custom_monitor": "val_f1"
}
[inference]
enabled: true
mode: "batch"
batch_size: 64
caching: true
cache_path: "/cache/predictions"
[performance]
optimization: true
optimization_level: "high"
quantization: true
quantization_type: "int8"
pruning: true
pruning_rate: 0.3
[monitoring]
prometheus_endpoint: "/actuator/prometheus"
enabled: true
labels {
    service: "user-prediction-service"
    version: "2.1.0"
    environment: @env("ENVIRONMENT", "production")
}
scrape_interval: 15
[alerting]
slack_webhook: @env.secure("SLACK_WEBHOOK")
email_endpoint: @env("ALERT_EMAIL")
[rules]
model_drift {
    condition: "prediction_drift > 0.1"
    threshold: "10%"
    duration: "1h"
    channels: ["slack", "email"]
    severity: "warning"
}
accuracy_drop {
    condition: "model_accuracy < 0.85"
    threshold: "85%"
    duration: "30m"
    channels: ["slack", "email"]
    severity: "critical"
}
training_failure {
    condition: "training_loss > 1.0"
    threshold: "1.0"
    duration: "10m"
    channels: ["slack"]
    severity: "warning"
}
Dynamic AI/ML configuration
[monitoring]
model_accuracy: @metrics("model_accuracy_percent", 0)
prediction_latency: @metrics("prediction_latency_ms", 0)
training_loss: @metrics("training_loss", 0)
validation_loss: @metrics("validation_loss", 0)
prediction_drift: @metrics("prediction_drift_score", 0)
data_quality_score: @metrics("data_quality_score", 0)
feature_importance: @metrics("feature_importance_score", 0)
model_version: @metrics("model_version", 0)

🔄 Neural Network Configuration

Deep Learning Setup

import org.tusklang.java.TuskLang;
import org.tusklang.java.config.TuskConfig;
@TuskConfig
public class NeuralNetworkConfig {
    private String type;
    private List<LayerConfig> layers;
    private OptimizerConfig optimizer;
    private LossConfig loss;
    private MetricsConfig metrics;
    
    // Getters and setters
    public String getType() { return type; }
    public void setType(String type) { this.type = type; }
    
    public List<LayerConfig> getLayers() { return layers; }
    public void setLayers(List<LayerConfig> layers) { this.layers = layers; }
    
    public OptimizerConfig getOptimizer() { return optimizer; }
    public void setOptimizer(OptimizerConfig optimizer) { this.optimizer = optimizer; }
    
    public LossConfig getLoss() { return loss; }
    public void setLoss(LossConfig loss) { this.loss = loss; }
    
    public MetricsConfig getMetrics() { return metrics; }
    public void setMetrics(MetricsConfig metrics) { this.metrics = metrics; }
}
@TuskConfig
public class LayerConfig {
    private String type;
    private int units;
    private String activation;
    private boolean dropout;
    private double dropoutRate;
    private boolean batchNormalization;
    
    // Getters and setters
    public String getType() { return type; }
    public void setType(String type) { this.type = type; }
    
    public int getUnits() { return units; }
    public void setUnits(int units) { this.units = units; }
    
    public String getActivation() { return activation; }
    public void setActivation(String activation) { this.activation = activation; }
    
    public boolean isDropout() { return dropout; }
    public void setDropout(boolean dropout) { this.dropout = dropout; }
    
    public double getDropoutRate() { return dropoutRate; }
    public void setDropoutRate(double dropoutRate) { this.dropoutRate = dropoutRate; }
    
    public boolean isBatchNormalization() { return batchNormalization; }
    public void setBatchNormalization(boolean batchNormalization) { this.batchNormalization = batchNormalization; }
}
@TuskConfig
public class OptimizerConfig {
    private String type;
    private double learningRate;
    private double momentum;
    private double weightDecay;
    private double epsilon;
    
    // Getters and setters
    public String getType() { return type; }
    public void setType(String type) { this.type = type; }
    
    public double getLearningRate() { return learningRate; }
    public void setLearningRate(double learningRate) { this.learningRate = learningRate; }
    
    public double getMomentum() { return momentum; }
    public void setMomentum(double momentum) { this.momentum = momentum; }
    
    public double getWeightDecay() { return weightDecay; }
    public void setWeightDecay(double weightDecay) { this.weightDecay = weightDecay; }
    
    public double getEpsilon() { return epsilon; }
    public void setEpsilon(double epsilon) { this.epsilon = epsilon; }
}
@TuskConfig
public class LossConfig {
    private String type;
    private Map<String, Object> parameters;
    
    // Getters and setters
    public String getType() { return type; }
    public void setType(String type) { this.type = type; }
    
    public Map<String, Object> getParameters() { return parameters; }
    public void setParameters(Map<String, Object> parameters) { this.parameters = parameters; }
}
@TuskConfig
public class MetricsConfig {
    private List<String> metrics;
    private Map<String, Object> customMetrics;
    
    // Getters and setters
    public List<String> getMetrics() { return metrics; }
    public void setMetrics(List<String> metrics) { this.metrics = metrics; }
    
    public Map<String, Object> getCustomMetrics() { return customMetrics; }
    public void setCustomMetrics(Map<String, Object> customMetrics) { this.customMetrics = customMetrics; }
}

neural-network.tsk

[neural_network]
type: "feedforward"
[layers]
input_layer {
    type: "dense"
    units: 128
    activation: "relu"
    dropout: true
    dropout_rate: 0.2
    batch_normalization: true
}
hidden_layer_1 {
    type: "dense"
    units: 64
    activation: "relu"
    dropout: true
    dropout_rate: 0.3
    batch_normalization: true
}
hidden_layer_2 {
    type: "dense"
    units: 32
    activation: "relu"
    dropout: true
    dropout_rate: 0.3
    batch_normalization: true
}
hidden_layer_3 {
    type: "dense"
    units: 16
    activation: "relu"
    dropout: true
    dropout_rate: 0.2
    batch_normalization: true
}
output_layer {
    type: "dense"
    units: 1
    activation: "sigmoid"
    dropout: false
    batch_normalization: false
}
[optimizer]
type: "adam"
learning_rate: @learn("optimal_learning_rate", "0.001")
momentum: 0.9
weight_decay: 0.0001
epsilon: 1e-8
[loss]
type: "binary_crossentropy"
parameters {
    "from_logits": false
    "label_smoothing": 0.0
}
[metrics]
metrics: [
    "accuracy",
    "precision",
    "recall",
    "f1_score",
    "auc"
]
custom_metrics {
    "custom_f1": "weighted_f1_score"
    "custom_auc": "roc_auc_score"
}
Neural network monitoring
[monitoring]
layer_activations: @metrics("layer_activation_stats", 0)
gradient_norms: @metrics("gradient_norm_stats", 0)
weight_distributions: @metrics("weight_distribution_stats", 0)

🎯 Best Practices

1. Model Design

- Choose appropriate architecture - Use proper activation functions - Implement regularization - Monitor model complexity

2. Data Management

- Ensure data quality - Implement proper preprocessing - Use data augmentation - Monitor data drift

3. Training

- Use appropriate hyperparameters - Implement early stopping - Monitor training progress - Use proper validation

4. Inference

- Optimize for performance - Implement caching - Monitor prediction quality - Handle edge cases

5. Monitoring

- Track model performance - Monitor data quality - Implement alerting - Version control models

🔧 Troubleshooting

Common Issues

1. Overfitting - Increase regularization - Add dropout layers - Use early stopping - Collect more data

2. Underfitting - Increase model complexity - Reduce regularization - Train longer - Add more features

3. Training Instability - Adjust learning rate - Use gradient clipping - Normalize data - Check data quality

4. Poor Performance - Feature engineering - Hyperparameter tuning - Model selection - Data preprocessing

Debug Commands

Check model performance
curl -X GET http://ai-service:8080/actuator/metrics/model.accuracy
Monitor training progress
tensorboard --logdir=/logs/training
Check data quality
python data_quality_check.py
Validate model predictions
curl -X POST http://ai-service:8080/api/predict -d '{"features": [...]}'

🚀 Next Steps

1. Deploy AI/ML models to production 2. Set up model monitoring and alerting 3. Implement A/B testing for models 4. Optimize inference performance 5. Monitor and retrain models

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