Session 1: Introduction to Deep Learning (1 Hour)

• AI vs. Machine Learning vs. Deep Learning
• Deep Learning Applications:
  • Computer Vision (Image classification, object detection)
  • Natural Language Processing (NLP) (Sentiment analysis, machine translation)
  • Speech Recognition
• Basic Neural Network Architecture

Session 2: Neural Networks (2 Hours)

• Perceptron & Multi-Layer Perceptron (MLP)
• Gradient Descent & Backpropagation
• Optimization Techniques: SGD, Adam
• Activation Functions: Sigmoid, ReLU, Tanh
• Loss Functions: MSE, Cross-Entropy

Hands-on Lab: Training a Neural Network using TensorFlow/Keras

Session 3: Deep Learning Frameworks (1 Hour)

• Installing & Setting Up TensorFlow/Keras
• Building a Simple Neural Network with Keras

Hands-on Lab: Train a Neural Network on the MNIST Dataset

Session 4: Convolutional Neural Networks (CNNs) (3 Hours)

• Convolutional Layers, Pooling & Feature Extraction
• CNN Applications: Image Classification, Object Detection

Hands-on Lab: Train a CNN on the CIFAR-10 Dataset