Description:

Step into the world of deep learning by building your first Artificial Neural Network!
In this 90-minute, beginner-friendly session, you’ll uncover how neural networks learn, why activation functions are critical, and how to build a real model using TensorFlow and Keras.
If you know machine learning basics but want to start constructing real deep learning models, this is the perfect launchpad.

Duration: 90 mins

Course Code: BDT493

Learning Objectives:

After this course, you will be able to:

• Demystifying Artificial Neural Networks (ANNs)
• How do ANNs learn?
• Building a basic ANN

• 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