๐๐จ๐ฐ ๐ญ๐จ ๐๐๐ฌ๐ข๐ ๐ง ๐ ๐๐๐ฎ๐ซ๐๐ฅ ๐๐๐ญ๐ฐ๐จ๐ซ๐ค
โ ๐๐๐๐ข๐ง๐ ๐ญ๐ก๐ ๐๐ซ๐จ๐๐ฅ๐๐ฆ
Clearly outline the type of task:
โฌ Classification: Predict discrete labels (e.g., cats vs dogs).
โฌ Regression: Predict continuous values
โฌ Clustering: Find patterns in unsupervised data.
โ ๐๐ซ๐๐ฉ๐ซ๐จ๐๐๐ฌ๐ฌ ๐๐๐ญ๐
Data quality is critical for model performance.
โฌ Normalize and standardize features MinMaxScaler, StandardScaler.
โฌ Handle missing values and outliers.
โฌ Split your data: Training (70%), Validation (15%), Testing (15%).
โ ๐๐๐ฌ๐ข๐ ๐ง ๐ญ๐ก๐ ๐๐๐ญ๐ฐ๐จ๐ซ๐ค ๐๐ซ๐๐ก๐ข๐ญ๐๐๐ญ๐ฎ๐ซ๐
๐ฐ๐ง๐ฉ๐ฎ๐ญ ๐๐๐ฒ๐๐ซ
โฌ Number of neurons equals the input features.
๐๐ข๐๐๐๐ง ๐๐๐ฒ๐๐ซ๐ฌ
โฌ Start with a few layers and increase as needed.
โฌ Use activation functions:
โ ReLU: General-purpose. Fast and efficient.
โ Leaky ReLU: Fixes dying neuron problems.
โ Tanh/Sigmoid: Use sparingly for specific cases.
๐๐ฎ๐ญ๐ฉ๐ฎ๐ญ ๐๐๐ฒ๐๐ซ
โฌ Classification: Use Softmax or Sigmoid for probability outputs.
โฌ Regression: Linear activation (no activation applied).
โ ๐๐ง๐ข๐ญ๐ข๐๐ฅ๐ข๐ณ๐ ๐๐๐ข๐ ๐ก๐ญ๐ฌ
Proper weight initialization helps in faster convergence:
โฌ He Initialization: Best for ReLU-based activations.
โฌ Xavier Initialization: Ideal for sigmoid/tanh activations.
โ ๐๐ก๐จ๐จ๐ฌ๐ ๐ญ๐ก๐ ๐๐จ๐ฌ๐ฌ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง
โฌ Classification: Cross-Entropy Loss.
โฌ Regression: Mean Squared Error or Mean Absolute Error.
โ ๐๐๐ฅ๐๐๐ญ ๐ญ๐ก๐ ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ซ
Pick the right optimizer to minimize the loss:
โฌ Adam: Most popular choice for speed and stability.
โฌ SGD: Slower but reliable for smaller models.
โ ๐๐ฉ๐๐๐ข๐๐ฒ ๐๐ฉ๐จ๐๐ก๐ฌ ๐๐ง๐ ๐๐๐ญ๐๐ก ๐๐ข๐ณ๐
โฌ Epochs: Define total passes over the training set. Start with 50โ100 epochs.
โฌ Batch Size: Small batches train faster but are less stable. Larger batches stabilize gradients.
โ ๐๐ซ๐๐ฏ๐๐ง๐ญ ๐๐ฏ๐๐ซ๐๐ข๐ญ๐ญ๐ข๐ง๐
โฌ Add Dropout Layers to randomly deactivate neurons.
โฌ Use L2 Regularization to penalize large weights.
โ ๐๐ฒ๐ฉ๐๐ซ๐ฉ๐๐ซ๐๐ฆ๐๐ญ๐๐ซ ๐๐ฎ๐ง๐ข๐ง๐
Optimize your model parameters to improve performance:
โฌ Adjust learning rate, dropout rate, layer size, and activations.
โฌ Use Grid Search or Random Search for hyperparameter optimization.
โ ๐๐ฏ๐๐ฅ๐ฎ๐๐ญ๐ ๐๐ง๐ ๐๐ฆ๐ฉ๐ซ๐จ๐ฏ๐
โฌ Monitor metrics for performance:
โ Classification: Accuracy, Precision, Recall, F1-score, AUC-ROC.
โ Regression: RMSE, MAE, Rยฒ score.
โ ๐๐๐ญ๐ ๐๐ฎ๐ ๐ฆ๐๐ง๐ญ๐๐ญ๐ข๐จ๐ง
โฌ For image tasks, apply transformations like rotation, scaling, and flipping to expand your dataset.
#artificialintelligence
โ ๐๐๐๐ข๐ง๐ ๐ญ๐ก๐ ๐๐ซ๐จ๐๐ฅ๐๐ฆ
Clearly outline the type of task:
โฌ Classification: Predict discrete labels (e.g., cats vs dogs).
โฌ Regression: Predict continuous values
โฌ Clustering: Find patterns in unsupervised data.
โ ๐๐ซ๐๐ฉ๐ซ๐จ๐๐๐ฌ๐ฌ ๐๐๐ญ๐
Data quality is critical for model performance.
โฌ Normalize and standardize features MinMaxScaler, StandardScaler.
โฌ Handle missing values and outliers.
โฌ Split your data: Training (70%), Validation (15%), Testing (15%).
โ ๐๐๐ฌ๐ข๐ ๐ง ๐ญ๐ก๐ ๐๐๐ญ๐ฐ๐จ๐ซ๐ค ๐๐ซ๐๐ก๐ข๐ญ๐๐๐ญ๐ฎ๐ซ๐
๐ฐ๐ง๐ฉ๐ฎ๐ญ ๐๐๐ฒ๐๐ซ
โฌ Number of neurons equals the input features.
๐๐ข๐๐๐๐ง ๐๐๐ฒ๐๐ซ๐ฌ
โฌ Start with a few layers and increase as needed.
โฌ Use activation functions:
โ ReLU: General-purpose. Fast and efficient.
โ Leaky ReLU: Fixes dying neuron problems.
โ Tanh/Sigmoid: Use sparingly for specific cases.
๐๐ฎ๐ญ๐ฉ๐ฎ๐ญ ๐๐๐ฒ๐๐ซ
โฌ Classification: Use Softmax or Sigmoid for probability outputs.
โฌ Regression: Linear activation (no activation applied).
โ ๐๐ง๐ข๐ญ๐ข๐๐ฅ๐ข๐ณ๐ ๐๐๐ข๐ ๐ก๐ญ๐ฌ
Proper weight initialization helps in faster convergence:
โฌ He Initialization: Best for ReLU-based activations.
โฌ Xavier Initialization: Ideal for sigmoid/tanh activations.
โ ๐๐ก๐จ๐จ๐ฌ๐ ๐ญ๐ก๐ ๐๐จ๐ฌ๐ฌ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง
โฌ Classification: Cross-Entropy Loss.
โฌ Regression: Mean Squared Error or Mean Absolute Error.
โ ๐๐๐ฅ๐๐๐ญ ๐ญ๐ก๐ ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ซ
Pick the right optimizer to minimize the loss:
โฌ Adam: Most popular choice for speed and stability.
โฌ SGD: Slower but reliable for smaller models.
โ ๐๐ฉ๐๐๐ข๐๐ฒ ๐๐ฉ๐จ๐๐ก๐ฌ ๐๐ง๐ ๐๐๐ญ๐๐ก ๐๐ข๐ณ๐
โฌ Epochs: Define total passes over the training set. Start with 50โ100 epochs.
โฌ Batch Size: Small batches train faster but are less stable. Larger batches stabilize gradients.
โ ๐๐ซ๐๐ฏ๐๐ง๐ญ ๐๐ฏ๐๐ซ๐๐ข๐ญ๐ญ๐ข๐ง๐
โฌ Add Dropout Layers to randomly deactivate neurons.
โฌ Use L2 Regularization to penalize large weights.
โ ๐๐ฒ๐ฉ๐๐ซ๐ฉ๐๐ซ๐๐ฆ๐๐ญ๐๐ซ ๐๐ฎ๐ง๐ข๐ง๐
Optimize your model parameters to improve performance:
โฌ Adjust learning rate, dropout rate, layer size, and activations.
โฌ Use Grid Search or Random Search for hyperparameter optimization.
โ ๐๐ฏ๐๐ฅ๐ฎ๐๐ญ๐ ๐๐ง๐ ๐๐ฆ๐ฉ๐ซ๐จ๐ฏ๐
โฌ Monitor metrics for performance:
โ Classification: Accuracy, Precision, Recall, F1-score, AUC-ROC.
โ Regression: RMSE, MAE, Rยฒ score.
โ ๐๐๐ญ๐ ๐๐ฎ๐ ๐ฆ๐๐ง๐ญ๐๐ญ๐ข๐จ๐ง
โฌ For image tasks, apply transformations like rotation, scaling, and flipping to expand your dataset.
#artificialintelligence