Module 1: Neural Networks and Deep Learning Architectures (10 hours) 

• Introduction to neural networks and deep learning 
• Feedforward neural networks and backpropagation 
• Activation functions and optimization techniques 
• Regularization and dropout 
• Model selection and Hyperparameter tuning 

• Hands-on project: Building and training a basic neural network. 

Module 2: Convolutional Neural Networks (CNNs) for Computer Vision (10 hours) 

• Introduction to CNNs and their applications 
• CNN architecture and layers (convolution, pooling, etc.) 
• Transfer learning and fine-tuning pre-trained models 

• Object detection and image segmentation with CNNs 
• Deep learning frameworks for computer vision (e.g., TensorFlow, PyTorch) 
• Hands-on project: Implementing a CNN for image classification. 

Module 3: Recurrent Neural Networks (RNNs) for Natural Language Processing (10 hours) 

• Introduction to RNNs and their applications 

• RNN architecture and types (LSTM, GRU) 
• Word embeddings and text preprocessing 
• Language modeling and sentiment analysis with RNNs 
• Text generation and machine translation 
• Hands-on project: Building a sentiment analysis model using RNNs. 

Module 4: Generative Models and Adversarial Networks (10 hours) 

• Introduction to generative models and their applications 
• Autoencoders and variational autoencoders (VAEs) 
• Generative adversarial networks (GANs) 
• Image generation and style transfer with GANs 

• Text synthesis and other applications of generative models 
• Hands-on project: Creating a generative adversarial network for image generation. 

Module 5: Real-World Projects (5 hours) 

• Applying deep learning techniques to real-world projects and applications. 
• Working on a comprehensive deep learning project to showcase learned skills. 
• Demonstration and presentation of project outcomes.