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SELECT 
    p.Id AS PostID,
    p.Title,
    p.Body,
    p.Score,
    p.ViewCount,
    p.ClosedDate,
    t.TagName,
    p.AcceptedAnswerId
FROM 
    Posts p
JOIN 
    PostTags pt ON p.Id = pt.PostId
JOIN 
    Tags t ON pt.TagId = t.Id
WHERE 
    p.ClosedDate IS NOT NULL -- Post is closed
    AND p.AcceptedAnswerId IS NOT NULL -- Post has an accepted answer (i.e., "fixed")
       AND (
        p.Title LIKE '%Activation Function%'
        OR p.Title LIKE '%Artificial Neural Network%'
        OR p.Title LIKE '%ANN%'
        OR p.Title LIKE '%Autoencoder%'
        OR p.Title LIKE '%Backpropagation%'
        -- (Continue adding all other terms as needed...)
        OR t.TagName IN (
            'Activation Function', 'Artificial Neural Network', 'ANN', 'Autoencoder', 'Backpropagation', 
            'Batch Normalization', 'Convolutional Neural Network', 'CNN', 'Cost Function', 'Deep Belief Network', 
            'DBN', 'Deep Learning', 'Dropout', 'Epoch', 'Fully Connected Layer', 'Generative Adversarial Network', 
            'GAN', 'Gradient Descent', 'Hyperparameter Tuning', 'tuning', 'Learning Rate', 'Long Short-Term Memory', 
            'LSTM', 'Loss Function', 'Max Pooling', 'Mini-Batch Gradient Descent', 'Multilayer Perceptron', 'MLP', 
            'Neural Network', 'Overfitting', 'Recurrent Neural Network', 'RNN', 'Rectified Linear Unit', 'ReLU', 
            'Residual Network', 'ResNet', 'Softmax', 'Stochastic Gradient Descent', 'SGD', 'Tensor', 
            'Transfer Learning', 'Vanishing Gradient Problem', 'Weight Initialization', 'Attention Mechanism', 
            'BERT', 'Bidirectional Encoder Representations from Transformers', 'Capsule Network', 'DNN', 
            'Deep Neural Network', 'Embedding', 'Encoder-Decoder Architecture', 'Fine-Tuning', 'Gated Recurrent Unit', 
            'GRU', 'Gradient Clipping', 'Graph Neural Network', 'GNN', 'L1 Regularization', 'L2 Regularization', 
            'Layer Normalization', 'Leaky ReLU', 'Learning Rate Scheduler', 'Momentum', 'Neural Architecture Search', 
            'NAS', 'Nesterov Accelerated Gradient', 'NAG', 'Normalization', 'One-Hot Encoding', 'Positional Encoding', 
            'Pre-trained Model', 'Reinforcement Learning', 'Self-Attention', 'Sequence-to-Sequence', 'Seq2Seq', 
            'Siamese Network', 'Sigmoid Activation', 'Spectral Normalization', 'Sparse Coding', 'Sparsity', 
            'Stochastic Depth', 'Support Vector Machine', 'SVM', 'Swish Activation', 'Temporal Convolutional Network', 
            'TCN', 'Tokenization', 'Transformer', 'U-Net', 'Upsampling', 'Variational Autoencoder', 'VAE', 
            'Weight Decay', 'Xavier Initialization', 'Zero Padding', 'AdaGrad', 'Adam Optimizer', 'AdamW Optimizer', 
            'Adversarial Training', 'Attention Layer', 'Bidirectional LSTM', 'Block Dropout', 'Causal Convolution', 
            'Char-RNN', 'Curriculum Learning', 'Data Augmentation', 'Data Pipeline', 'Denoising Autoencoder', 'DAE', 
            'Dense Layer', 'DropConnect', 'ELMo', 'Embeddings from Language Models', 'Keras', 'Keras Layers', 
            'Keras Callbacks', 'Keras Sequential Model', 'Keras Functional API', 'ModelCheckpoint', 'EarlyStopping', 
            'TensorFlow', 'TensorBoard', 'Keras Optimizers', 'Keras Losses', 'Keras Metrics', 'Keras Model Subclassing', 
            'Keras Custom Layers', 'Keras Model Compilation', 'Keras Fit', 'Keras Evaluate', 'Keras Predict', 
            'fit', 'predict'
        )
    );

fork query

edited aug 12

created jul 5 anonymous