Introduction to Deep Learning

• What is deep learning? Overview of its applications in AI
• Difference between traditional machine learning and deep learning
• Introduction to neural networks: perceptron and multi-layer networks

Neural Networks and Backpropagation

• Understanding the architecture of neural networks
• Activation functions: sigmoid, ReLU, and softmax
• Forward propagation and backpropagation: how neural networks learn

Convolutional Neural Networks (CNNs)

• Basics of CNNs and their applications in computer vision
• Understanding convolution layers, pooling, and fully connected layers
• Building image classification models with CNNs

Recurrent Neural Networks (RNNs) and LSTMs

• Introduction to RNNs for sequence prediction
• Long Short-Term Memory (LSTM) networks and GRUs
• Applications of RNNs in natural language processing and time series forecasting

Deep Learning Frameworks

• Introduction to TensorFlow and Keras
• Building and training deep learning models using TensorFlow and Keras
• Model evaluation and optimization

Generative Models and GANs

• Introduction to generative models and their applications
• Working with Generative Adversarial Networks (GANs)
• Building generative models for image generation and style transfer

Advanced Deep Learning Techniques

• Transfer learning and fine-tuning pre-trained models
• Regularization techniques: dropout, batch normalization
• Hyperparameter tuning and model optimization

Deployment of Deep Learning Models

• Deploying deep learning models on cloud platforms
• Using APIs for real-time AI applications
• Integrating models into production environments

Capstone Project: End-to-End Deep Learning Model

• Develop a deep learning model from scratch
• Work on real-world datasets to apply deep learning techniques
• Present findings and deploy the final model