Machine Learning is growing by leaps and bounds with new neural network models coming up regularly. These models are trained for a specific dataset and are proven for accuracy and processing speed. Developers need to evaluate ML models and ensure that they meet specific threshold values and functions as expected before deployment. There is a lot of experimenting going into improving the model performance, and visualizing differences become crucial while designing and training a model. TensorBoard helps visualize the model, making the analysis less complicated, as debugging becomes easier when one can see what the problem is.
General Practice to Train ML Models
The general practice is to use pre-trained models and perform Transfer Learning to re-train the model for a similar set of data. In a technique called Transfer Learning, a neural network model is first trained on a problem similar to the one that is being solved. One or more layers from the trained model are then used in a new model trained on the problem of interest.