Generative AI

Embarking on a journey to excel in Generative AI is a rewarding endeavour. Below is a detailed curriculum designed to guide you from a beginner to an advanced level in Generative AI over a span of six months. The curriculum is broken down into weekly segments, with daily tasks to keep you on track. Adjust the pace as needed to fit your schedule and comprehension speed.

Month 1: Foundations of AI and Machine Learning

Week 1-2: Introduction to AI and Machine Learning

    • Day 1-2: Understand the basics of AI and ML.
    • Day 3-4: Learn about different types of machine learning (supervised, unsupervised, reinforcement learning).
      • Read: “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron (Chapters 1-2)
      • Practice: Basic ML algorithms using scikit-learn (linear regression, classification).
    • Day 5-7: Introduction to Python for AI.
      • Complete Python basics on Codecademy or freeCodeCamp.
      • Practice: Simple Python projects and exercises on platforms like LeetCode or HackerRank.

Week 3-4: Deep Learning Fundamentals

    • Day 8-10: Learn the basics of neural networks.
      • Read: “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (Chapters 1-3)
      • Watch: Deep Learning Specialization by Andrew Ng on Coursera (Course 1: Neural Networks and Deep Learning).
    • Day 11-14: Implement basic neural networks in Python using TensorFlow/Keras.
      • Practice: Build and train a simple neural network for a classification task.
    • Day 15-18: Understand convolutional neural networks (CNNs).
      • Read: “Deep Learning” by Ian Goodfellow (Chapter 9)
      • Practice: Implement a CNN for image classification (e.g., MNIST dataset).
    • Day 19-21: Explore recurrent neural networks (RNNs) and LSTMs.
      • Read: “Deep Learning” by Ian Goodfellow (Chapter 10)
      • Practice: Build an RNN for sequence prediction.

Month 2: Introduction to Generative AI

Week 5-6: Generative Models Basics

    • Day 22-24: Understand the concepts of generative models.
      • Read: Research papers on generative models (e.g., Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs)).
      • Watch: Relevant lectures from Stanford’s CS231n and CS224n.
    • Day 25-28: Dive into Variational Autoencoders (VAEs).
      • Read: Original VAE paper by Kingma and Welling.
      • Practice: Implement a VAE in TensorFlow/Keras.
    • Day 29-31: Explore Generative Adversarial Networks (GANs).
      • Read: Goodfellow et al.’s original GAN paper.
      • Watch: Ian Goodfellow’s NIPS 2016 tutorial on GANs.

Week 7-8: Hands-on with GANs and VAEs

    • Day 32-35: Implement a basic GAN.
      • Practice: Build and train a GAN to generate simple images (e.g., MNIST).
      • Experiment with different architectures and hyperparameters.
    • Day 36-38: Implement a conditional GAN (cGAN).
      • Read: Mirza and Osindero’s cGAN paper.
      • Practice: Build and train a cGAN for generating labeled images.
    • Day 39-42: Explore and implement advanced GAN variants.
      • Read: Papers on DCGAN, WGAN, and CycleGAN.
      • Practice: Implement and experiment with one or more advanced GANs.

Month 3: Advanced Topics in Generative AI

Week 9-10: Attention Mechanisms and Transformers

    • Day 43-46: Understand attention mechanisms.
      • Read: “Attention is All You Need” by Vaswani et al.
      • Watch: Relevant lectures from CS224n.
    • Day 47-50: Implement a simple transformer model.
      • Practice: Build a transformer for text generation or translation.
    • Day 51-54: Explore BERT, GPT, and other transformer-based models.
      • Read: Papers on BERT, GPT-2, and GPT-3.
      • Practice: Use Hugging Face’s Transformers library to fine-tune a pre-trained model.

Week 11-12: Reinforcement Learning for Generative AI

    • Day 55-58: Understand the basics of reinforcement learning (RL).
      • Read: “Reinforcement Learning: An Introduction” by Sutton and Barto (Chapters 1-4).
      • Watch: David Silver’s RL course on YouTube.
    • Day 59-62: Explore applications of RL in generative models.
      • Read: Papers on using RL for text generation and GANs.
      • Practice: Implement a simple RL algorithm (e.g., Q-learning) and experiment with it.

Month 4: Practical Applications and Projects

Week 13-14: Text Generation and NLP Applications

    • Day 63-66: Dive into text generation techniques.
      • Read: Papers on RNNs, LSTMs, and transformers for text generation.
      • Practice: Build a text generation model using GPT-2 or GPT-3.
    • Day 67-70: Implement chatbots and dialogue systems.
      • Practice: Use a transformer-based model to create a simple chatbot.
      • Experiment with fine-tuning for specific dialogue tasks.

Week 15-16: Image and Video Generation

    • Day 71-74: Explore image generation techniques.
      • Read: Papers on StyleGAN and other state-of-the-art image generation models.
      • Practice: Implement and experiment with a StyleGAN model.
    • Day 75-78: Explore video generation and enhancement techniques.
      • Read: Research on video GANs and related models.
      • Practice: Implement a simple video generation or enhancement model.

Month 5: Advanced Projects and Research

Week 17-18: Complex Projects and Integration

    • Day 79-84: Choose and start a complex project.
      • Project ideas: Text-to-image generation, music generation, art creation.
      • Break down the project into smaller tasks and set milestones.
    • Day 85-90: Continue working on the project.
      • Regularly review and debug your progress.
      • Document your work and results.

Week 19-20: Advanced Research and State-of-the-Art

    • Day 91-95: Review recent research papers.
      • Use platforms like arXiv, Google Scholar to find recent papers in generative AI.
      • Summarize key findings and techniques from the papers.
    • Day 96-100: Experiment with state-of-the-art models.
      • Implement and test models from the latest research.
      • Compare their performance with previous models you have built.

Month 6: Refinement and Specialization

Week 21-22: Refinement and Optimization

    • Day 101-105: Focus on model optimization techniques.
      • Read: Papers on model optimization and efficiency (e.g., pruning, quantization).
      • Practice: Optimize one of your previous models for better performance.
    • Day 106-110: Experiment with deployment.
      • Learn: Basics of deploying ML models using cloud services (e.g., AWS, Google Cloud).
      • Practice: Deploy a generative model as a web service or app.

Week 23-24: Specialization and Future Directions

    • Day 111-115: Choose a specialization within generative AI.
      • Specializations could include: Text generation, image synthesis, music generation, etc.
      • Deep dive into specialized literature and advanced techniques in your chosen area.
    • Day 116-120: Plan for future learning and research.
      • Identify gaps in your knowledge and plan further studies.
      • Connect with the AI community through conferences, forums, and collaboration on projects.

Ongoing: Continuous Learning and Community Involvement

    • Weekly: Keep up with the latest research by reading new papers on arXiv.
    • Monthly: Participate in online AI forums and discussions (e.g., Reddit, Stack Overflow, specialized Discord servers).
    • Yearly: Attend AI conferences and workshops to stay updated and network with other professionals.

By following this comprehensive curriculum, you will build a strong foundation in Generative AI and gradually advance to tackling complex projects and contributing to cutting-edge research. Remember to stay curious, seek feedback, and continuously iterate on your learning process.