COURSE OUTLINE

Day 1: Introduction to Deep Learning

Focus: Understanding the foundations of neural networks

• Introduction to Artificial Intelligence, Machine Learning, and Deep Learning
• Biological inspiration behind neural networks 
• Structure of a neural network (input, hidden, output layers) 
• Introduction to neurons, weights, and biases 
• Overview of deep learning frameworks (TensorFlow, Keras, PyTorch) 
• Deep learning use cases in real-world applications 
• Hands-on: Setting up deep learning environment in Python

 Day 2: Building Neural Networks

Focus: Understanding how neural networks learn

• Forward propagation process 
• Backpropagation and gradient descent optimization 
• Activation functions (Sigmoid, ReLU, Tanh, Softmax) 
• Loss functions and cost optimization 
• Training and evaluating neural networks 
• Overfitting and underfitting concepts 
• Hands-on: Building and training a simple neural network

Day 3: Convolutional Neural Networks (CNNs)

Focus: Deep learning for image data

• Introduction to CNN architecture 
• Convolution, pooling, and feature extraction concepts 
• Understanding filters and feature maps 
• Image classification workflow 
• Data augmentation techniques 
• Transfer learning and pre-trained models (e.g., VGG, ResNet) 
• Hands-on: Building an image classification model using CNN

Day 4: Recurrent Neural Networks (RNNs)

Focus: Deep learning for sequential data

• Introduction to sequence modeling problems 
• RNN architecture and working principle 
• Limitations of basic RNNs (vanishing gradient problem) 
• LSTM (Long Short-Term Memory) networks 
• GRU (Gated Recurrent Units) overview 
• Time series forecasting basics 
• Hands-on: Building an RNN/LSTM model for sequence prediction

Day 5: Advanced Deep Learning Techniques

Focus: Exploring modern and generative models

• Introduction to Generative Adversarial Networks (GANs) 
• Generator vs Discriminator architecture 
• Autoencoders and their applications 
• Variational Autoencoders (VAE) overview 
• Model optimization techniques 
• Regularization methods (Dropout, Batch Normalization) 
• Hands-on: Simple autoencoder or GAN demonstration

Day 6: Final Project and Deployment

Focus: End-to-end implementation and real-world application

• Designing a complete deep learning project pipeline 
• Data preparation and model selection strategies 
• Model training, tuning, and evaluation 
• Introduction to model deployment concepts (APIs, cloud deployment basics) 
• Performance monitoring and improvement strategies 
• Final capstone project presentation 
• Assessment and feedback session