𝐇𝐨𝐰 𝐭𝐨 𝐃𝐞𝐬𝐢𝐠𝐧 𝐚 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤→ 𝐃𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦
Clearly outline the type of task:
↬ Classification: Predict discrete labels (e.g., cats vs dogs).
↬ Regression: Predict continuous values
↬ Clustering: Find patterns in unsupervised data.
→ 𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐃𝐚𝐭𝐚
Data quality is critical for model performance.
↬ Normalize and standardize features MinMaxScaler, StandardScaler.
↬ Handle missing values and outliers.
↬ Split your data: Training (70%), Validation (15%), Testing (15%).
→ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐞 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞
𝑰𝐧𝐩𝐮𝐭 𝐋𝐚𝐲𝐞𝐫
↬ Number of neurons equals the input features.
𝐇𝐢𝐝𝐝𝐞𝐧 𝐋𝐚𝐲𝐞𝐫𝐬
↬ Start with a few layers and increase as needed.
↬ Use activation functions:
→ ReLU: General-purpose. Fast and efficient.
→ Leaky ReLU: Fixes dying neuron problems.
→ Tanh/Sigmoid: Use sparingly for specific cases.
𝐎𝐮𝐭𝐩𝐮𝐭 𝐋𝐚𝐲𝐞𝐫
↬ Classification: Use Softmax or Sigmoid for probability outputs.
↬ Regression: Linear activation (no activation applied).
→ 𝐈𝐧𝐢𝐭𝐢𝐚𝐥𝐢𝐳𝐞 𝐖𝐞𝐢𝐠𝐡𝐭𝐬
Proper weight initialization helps in faster convergence:
↬ He Initialization: Best for ReLU-based activations.
↬ Xavier Initialization: Ideal for sigmoid/tanh activations.
→ 𝐂𝐡𝐨𝐨𝐬𝐞 𝐭𝐡𝐞 𝐋𝐨𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧
↬ Classification: Cross-Entropy Loss.
↬ Regression: Mean Squared Error or Mean Absolute Error.
→ 𝐒𝐞𝐥𝐞𝐜𝐭 𝐭𝐡𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐫
Pick the right optimizer to minimize the loss:
↬ Adam: Most popular choice for speed and stability.
↬ SGD: Slower but reliable for smaller models.
→ 𝐒𝐩𝐞𝐜𝐢𝐟𝐲 𝐄𝐩𝐨𝐜𝐡𝐬 𝐚𝐧𝐝 𝐁𝐚𝐭𝐜𝐡 𝐒𝐢𝐳𝐞
↬ Epochs: Define total passes over the training set. Start with 50–100 epochs.
↬ Batch Size: Small batches train faster but are less stable. Larger batches stabilize gradients.
→ 𝐏𝐫𝐞𝐯𝐞𝐧𝐭 𝐎𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠
↬ Add Dropout Layers to randomly deactivate neurons.
↬ Use L2 Regularization to penalize large weights.
→ 𝐇𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫 𝐓𝐮𝐧𝐢𝐧𝐠
Optimize your model parameters to improve performance:
↬ Adjust learning rate, dropout rate, layer size, and activations.
↬ Use Grid Search or Random Search for hyperparameter optimization.
→ 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐚𝐧𝐝 𝐈𝐦𝐩𝐫𝐨𝐯𝐞
↬ Monitor metrics for performance:
→ Classification: Accuracy, Precision, Recall, F1-score, AUC-ROC.
→ Regression: RMSE, MAE, R² score.
→ 𝐃𝐚𝐭𝐚 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧
↬ For image tasks, apply transformations like rotation, scaling, and flipping to expand your dataset.
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