COURSES / GENERATIVE AI
Topics - Introduction to Generative AI, Fundamentals of Machine Learning, Deep Learning Basics, Introduction to Neural Networks, Generative Models: Overview, Autoencoders and Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers and Language Models, Fine-tuning Pre-trained Models, Project and Application Development
Back to Courses| Generative AI | ||
| Day | Topics | Activities |
| Day 1 | Introduction to Generative AI | Overview of Generative AI applications and use cases. Study key concepts and terminologies. |
| Day 2 | Fundamentals of Machine Learning | Learn about supervised, unsupervised, and reinforcement learning. Explore key ML algorithms. |
| Day 3 | Deep Learning Basics | Introduction to deep learning frameworks (TensorFlow, PyTorch). Understand the basics of deep learning. |
| Day 4 | Introduction to Neural Networks | Build and train simple neural networks. Understand activation functions, loss functions, and optimizers. |
| Day 5 | Generative Models: Overview | Study different types of generative models (VAEs, GANs, Transformers). Understand their use cases. |
| Day 6 | Autoencoders and Variational Autoencoders (VAEs) | Implement basic autoencoders and VAEs. Explore their applications in data generation and reconstruction. |
| Day 7 | Generative Adversarial Networks (GANs) | Understand the architecture of GANs. Implement a simple GAN for image generation. |
| Day 8 | Transformers and Language Models | Learn about Transformers and their applications in NLP. Study models like GPT and BERT. |
| Day 9 | Fine-tuning Pre-trained Models | Fine-tune pre-trained models for specific tasks. Understand transfer learning and its applications. |
| Day 10 | Project and Application Development | Develop a project using generative models. Present findings and applications of the developed model. |
Introduction Lesson
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Topics - Introduction to Generative AI, Fundamentals of Machine Learning, Deep Learning Basics, Introduction to Neural Networks, Generative Models: Overview, Autoencoders and Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers and Language Models, Fine-tuning Pre-trained Models, Project and Application Development
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Emilly Blunt
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