• Learners familiar with concepts like model layers, activation functions, and compiling a model

• Machine learning students and practitioners who know how to build basic Keras models. Ideal for those who want to go beyond “default settings” and fine-tune their training process for better results

Course Outline:

1. Training Hyper Parameters
   • What are hyper parameters?
   • Understanding various hyper parameters for neural networks
   • Epochs, Number of layers, number of neurons
2. Choosing Optimizers and Right Loss Function
   • What an optimizer does: updating weights to minimize loss
   • Key Keras Optimizers: SGD, Adam, RMSprop
   • Learning rate: most important optimizer parameter
   • Why does loss function matter?
   • Loss functions: Binary, Categorical Cross Entropy, Mean Squared Error
3. Tracking training progress and metrics
   • Metrics ≠ Loss, metrics tell you “How good” the model is from user’s perspective
   • Common metrics: accuracy, precision, recall, AUC, MSE
4. Making training smarter with Callbacks
   • What are callbacks: functions trigged during training
   • Keras callbacks: Early Stopping, Model Check point, Reduce LR on Plateau
   • Hands-on: Use different callbacks during model training

Training material provided: Yes (Digital format)

Hands-on Lab: Instructions will be provided to install Jupyter notebook and other required python libraries. Students can opt to use ‘Google Colaboratory’ if they do not want to install these tools