knowledge Transfer

The optimal learning rate will be dependent on both your model architecture and your dataset. While using a default learning rate may provide decent results, you can often improve the performance or speed up training by searching for an optimal learning rate.

We don’t explicitly define the filters that our convolutional layer will use, instead parameterize the filters and let the network learn the best filters to use during training. We need to define “how many filters we’ll use at each layer”.

The choice of loss function must specific to the problem, such as binary, multi-class, or multi-label classification. Further, the configuration of the output layer must also be appropriate for the chosen loss function.

We use something called samplers for OverSampling. Though we did not use samplers exclusively, PyTorch used it for us internally. When we say shuffle=False, PyTorch ended up using SequentialSampler it gives an index from zero to the length of the dataset. When shuffle=True it ends up using a RandomSampler.

classification layer of the pre-trained model is specific to the original classification task, and subsequently specific to the set of classes on which the model was trained. You simply add a new classifier layer, which will be trained from scratch.

how to create a loss function for an imbalanced dataset in which minority class proportionally to its underrepresentation. You will use PyTorch to define the loss function and class weights to help the model learn from the imbalanced data.

You can learn a lot about Keras models by observing their History objects after training. In this post, you will discover how you can save the history object into a CSV file of deep learning models training metrics over time during training.

Here, we will be able to see our network graph. One of TensorBoard’s strengths is its ability to visualize complex model structures.

How to Collect and review metrics during the training of your deep learning models and how to plots from the data collected during training

The network training converges faster if its weights are linearly transformed to have zero means and unit variances.