深度学习模型优化和调整

上次更新时间：2024-09-10

课程售价: 2.9 元

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课程内容

0 - Introduction

1 - Introduction to Deep Learning Optimization

1. What is deep learning
2. Review of artificial neural networks
3. An ANN model
4. Model optimization and tuning
5. The deep learning tuning process
6. Experiment setups for the course

2 - Tuning the Deep Learning Network

1. Epoch and batch size tuning
2. Epoch and batch size experiment
3. Hidden layers tuning
4. Determining nodes in a layer
5. Choosing activation functions
6. Initializing weights

3 - Tuning Back Propagation

1. Vanishing and exploding gradients
2. Batch normalization
3. Optimizers
4. Optimizer experiment
5. Learning rate
6. Learning rate experiment

4 - Overfitting Management

1. Overfitting in ANNs
2. Regularization
3. Regularization experiment
4. Dropouts
5. Dropout experiment

5 - Model Tuning Exercise

1. Tuning exercise Problem statement
2. Acquire and process data
3. Tuning the network
4. Tuning backpropagation
5. Avoiding overfitting
6. Building the final model

6 - Conclusion

1. Continuing your deep learning journey

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深度学习模型优化和调整

上次更新时间：2024-09-10 16:07

课程售价：2.9元

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课程内容

7个章节 , 33个讲座

0 - Introduction

1 - Introduction to Deep Learning Optimization

1. What is deep learning
2. Review of artificial neural networks
3. An ANN model
4. Model optimization and tuning
5. The deep learning tuning process
6. Experiment setups for the course

2 - Tuning the Deep Learning Network

1. Epoch and batch size tuning
2. Epoch and batch size experiment
3. Hidden layers tuning
4. Determining nodes in a layer
5. Choosing activation functions
6. Initializing weights

3 - Tuning Back Propagation

1. Vanishing and exploding gradients
2. Batch normalization
3. Optimizers
4. Optimizer experiment
5. Learning rate
6. Learning rate experiment

4 - Overfitting Management

1. Overfitting in ANNs
2. Regularization
3. Regularization experiment
4. Dropouts
5. Dropout experiment

5 - Model Tuning Exercise

1. Tuning exercise Problem statement
2. Acquire and process data
3. Tuning the network
4. Tuning backpropagation
5. Avoiding overfitting
6. Building the final model

6 - Conclusion

1. Continuing your deep learning journey