Day 1
Session 1: Introduction to Neural Networks and Deep Learning

• What are neural networks and how do they work?
• Key components: Neurons, layers, and activation functions
• Overview of deep learning and its applications
• Hands-on lab: Visualizing a simple neural network

Session 2: Setting Up Keras for Deep Learning

• Overview of TensorFlow and Keras
• Installing and configuring Keras for neural network development
• Hands-on lab: Setting up your environment for Keras

Session 3: Building Your First Neural Network with Keras

• Understanding the Sequential API in Keras
• Creating a basic feedforward neural network
• Hands-on lab: Implementing and training a neural network

Day 2
Session 1: Training and Evaluating Neural Networks

• Loss functions and optimization algorithms
• Evaluating model performance with accuracy and other metrics
• Hands-on lab: Training and evaluating a neural network

Session 2: Improving Neural Network Performance

• Techniques for improving performance: Dropout, batch normalization
• Tuning hyperparameters for optimal results
• Hands-on lab: Enhancing a neural network model

Session 3: Working with Real-World Data

• Preprocessing data for deep learning models using Keras
• Managing large datasets with data generators
• Hands-on lab: Preparing and using a real-world dataset

Day 3
Session 1: Deep Learning for Classification and Regression

• Building models for classification tasks
• Adapting neural networks for regression problems
• Hands-on lab: Developing a neural network for a real-world task

Session 2: Case Study: Solving a Problem with Neural Networks

• Applying learned techniques to a comprehensive dataset
• Group activity: Building and presenting a neural network solution