Week 1: Deep Learning Specializations

• 🎥 Advanced Deep Learning Architectures (CNNs, RNNs, GANs)
• 🎥 Transfer Learning and Pre-trained Models (BERT, GPT)
• 🎥 Implementing Custom Layers and Loss Functions
• 🎥 Sequence Modeling (LSTM, GRU, Transformers)
• 🎥 Advanced Techniques in CNNs (ResNet, Inception)
• 📋 Assignment Week 1: Build and Optimize Advanced Deep Learning Models

Week 2: Computer Vision

• 🎥 Introduction to Computer Vision Concepts
• 🎥 Convolutional Neural Networks (CNNs): Understanding layers, filters, and feature learning, with a focus on VGGNet architecture
• 🎥 Object Detection: Techniques and algorithms, with a detailed look at YOLO (You Only Look Once) for real-time object detection
• 🎥 Object Tracking: Exploring methods and challenges in tracking objects across frames in video
• 🎥 Practical Work: Hands-on project implementing VGGNet on a dataset, followed by object detection and tracking on video data
• 📋 Assignment Week 2: Apply Computer Vision Techniques

Week 3: Generative Models

• 🎥 AutoEncoders: Understanding the architecture and applications of autoencoders in noise reduction and dimensionality reduction
• 🎥 Variational AutoEncoders (VAEs): Learn about VAEs for generating complex datasets
• 🎥 Deep Convolutional Generative Adversarial Networks (DCGANs): Focus on improving image quality and training stability
• 🎥 Style GANs: Explore the capabilities of Style GANs in generating photorealistic images
• 🎥 Practical Work: Implementing a DCGAN and a Style GAN to generate new images, comparing the outputs and discussing their uses
• 📋 Assignment Week 3: Implement Generative Models

Week 4: Diffusion Models

• 🎥 Fundamentals of Diffusion Models: Understanding the theoretical underpinnings and how they differ from and improve upon other generative models
• 🎥 Applications of Diffusion Models: Discuss practical applications in various fields such as art generation, super-resolution, and more
• 🎥 Practical Work: Building a simple diffusion model to generate images or enhance image quality
• 📋 Assignment Week 4: Apply Diffusion Model Techniques

Week 5: Large Language Models (LLMs)

• 🎥 Overview of LLMs: Understanding the architecture and capabilities of models like LLaMA and GPT (Generative Pre-trained Transformer)
• 🎥 LangChain: Integrating language models into applications
• 🎥 Retrieval-Augmented Generation (RAG): Combining the power of retrieval with generation for more informed and accurate outputs
• 🎥 Practical Work: Implementing a small-scale GPT model for a specific application, exploring LangChain, and experimenting with RAG for a question-answering system
• 📋 Assignment Week 5: Develop Applications Using LLMs

Week 6: Capstone Project - End-to-End Real-World Application

• 🎥 Application of skills learned to develop a comprehensive project that solves a real-world problem using LLMs, computer vision, and generative models
• 🎥 Practical Work: Develop and present a complete data science solution, integrating concepts from deep learning specializations, computer vision, generative models, diffusion models, and LLMs
• 📋 Final Project Submission and Presentation