• Must have some python programming experience and NumPy. Students must be comfortable with TensorFlow basics

Machine learning enthusiasts and practitioners who are familiar with basic ML concepts (e.g., models, datasets, training loops). Students new to deep learning and neural networks, wanting a clear and intuitive understanding of how networks learn

Course Outline:

1. Demystifying Artificial Neural Networks
   • What is a neural network? How does it mimic the brain?
   • Core components of neural network: neurons, layers, weights, biases
   • Understanding Forward and Backward pass
2. How do networks learn? Loss and Optimization
   • The learning goal: minimizing the loss function
   • Loss functions: MSE, Cross Entropy
   • Optimizers: Adam, SGD
   • Hands-on: Creating and Visualizing Loss
3. Building first neural network with Keras
   • A brief introduction to Keras high-level API
   • Build a simple neural network
   • Compiling the model (specifying Loss and Optimizer)
   • Hands-on: Build a simple neural network

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