Module 1: Introduction to Deep Learning and Neural Networks

• Overview of deep learning and its applications
• Biological inspiration: the neuron and neural networks
• Feedforward neural networks and backpropagation
• Activation functions and loss functions
• Practical: Implementing a simple neural network from scratch in Python

Module 2: PyTorch Fundamentals

• Introduction to PyTorch and its ecosystem
• Tensors, operations, and autograd
• Building and training neural networks in PyTorch
• Data loading and preprocessing with PyTorch
• Practical: Creating and training a feedforward neural network using PyTorch

Module 3: Transformers: Foundations and Components

• Introduction to the Transformer architecture
• Self-attention mechanisms and multi-head attention
• Positional encoding
• Transformer encoder and decoder
• Practical: Implementing a basic Transformer model

Module 4: Generative Transformers

• Introduction to generative tasks
• GPT (Generative Pre-trained Transformer) and its variants
• BERT (Bidirectional Encoder Representations from Transformers)
• Fine-tuning vs. pre-training
• Practical: Fine-tuning a pre-trained GPT-2 model for text generation

Module 5: Applications of Generative Transformers

• Natural Language Processing (NLP) tasks (e.g., text generation, translation, summarization)
• Computer Vision applications (e.g., image generation)
• Reinforcement Learning and Generative models
• Ethical considerations in generative AI
• Practical: Implementing a generative model for a specific application

Module 6: Capstone Project

• Students work on a substantial project applying Generative Transformer models to a real-world problem.
• Regular progress presentations and peer feedback sessions.
• Project presentation and documentation.

1. Professionals with a background in Python, data science, and machine learning who want to dive into the world of Generative Transformer models.
2. Data scientists and NLP practitioners seeking to expand their knowledge and practical skills in Generative Transformer models, especially GPT variants.
3. AI researchers and enthusiasts interested in staying up-to-date with the latest advancements in AI, specifically in the field of generative models.
4. Individuals looking to strengthen their expertise in AI, NLP, and generative modeling for career growth or project development.
5. Chatbot developers and language processing engineers aiming to leverage GPT models for building advanced conversational agents and language-related applications.
6. Graduate and undergraduate students who want to explore the capabilities and applications of Generative Transformer models in real-world scenarios.
7. Professionals and learners keen on understanding the evolving landscape of AI and the ethical considerations associated with the use of GPT models.

1. Proficiency in Python programming, including but not limited to:
   • Experience in working with Python libraries commonly used in machine learning and deep learning, such as NumPy, Pandas, and Matplotlib.
   • Ability to write and understand Python code for data manipulation and analysis.
2. A solid foundation in data science, which includes:
   • Experience with data cleaning, preprocessing, and exploratory data analysis (EDA).
   • Familiarity with data visualization techniques and tools.
   • Basic statistical knowledge.
3. A fundamental understanding of machine learning concepts, such as:
   • Supervised and unsupervised learning.
   • Model evaluation and validation techniques.
   • Feature engineering.

1. Build their own GPT models for specific tasks and projects.
2. Tune existing GPT models for custom applications and fine-tuning tasks.
3. Explore more advanced NLP techniques using GPT models, such as sequence-to-sequence models.
4. Develop chatbots and conversational agents using GPT models.
5. Perform sentiment analysis using GPT models.
6. Use GPT models for language translation and text generation.
7. Stay up-to-date with the latest advancements in AI and GPT models keeping up with relevant research.