Deep Neural Networks

Lecture 1: Introduction to Deep Learning

• Understanding Neural Networks
• History and Evolution of Deep Learning
• Applications of Deep Neural Networks
• Prerequisites and Required Tools

Lecture 2: Neural Network Fundamentals

• Perceptrons and Activation Functions
• Building Blocks: Neurons, Layers, and Weights
• Feedforward Propagation
• Loss Functions and Optimization

Lecture 3: Building Your First Neural Network

• Setting up Python and TensorFlow
• Creating a Simple Feedforward Neural Network
• Training a Model
• Evaluation and Testing

Lecture 4: Advanced Architectures

• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory (LSTM) Networks
• Gated Recurrent Unit (GRU) Networks

Lecture 5: Training Techniques and Regularization

• Batch Normalization
• Dropout and Weight Regularization
• Learning Rate Schedules
• Gradient Clipping

Lecture 6: Transfer Learning and Pre-trained Models

• Introduction to Transfer Learning
• Fine-tuning Pre-trained Models
• Using Popular Pre-trained Models

Lecture 7: Advanced Topics in Deep Learning

• Generative Adversarial Networks (GANs)
• Autoencoders and Variational Autoencoders (VAEs)
• Reinforcement Learning with Deep Neural Networks

Lecture 8: Deep Learning for Specific Applications

• Natural Language Processing (NLP) with RNNs and Transformers
• Computer Vision with CNNs and Object Detection
• Speech Recognition and Audio Processing
• Recommender Systems and Personalization

Lecture 9: Model Interpretability and Visualization

• Interpreting Deep Learning Models
• Visualizing Activation Maps and Feature Maps
• Tools for Model Interpretability