6
Lessons
14 hours
Duration
English
Language
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OBJECTIVE:
- This course is tailored for senior engineers seeking an in-depth understanding of generative artificial intelligence (AI) techniques and algorithms.
- Participants will delve into advanced generative models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and autoregressive models. The course includes hands-on labs to provide practical experience in building and training sophisticated generative AI models.
- This two-day course provides senior engineers with a deep dive into advanced generative AI concepts and techniques. Through a combination of lectures, hands-on labs, and discussions, participants will gain practical experience and expertise in building and deploying sophisticated generative AI models for various applications.
Course features:
- Practical hands on
- Lab sessions
- Training by experienced faculty
PRE-REQUISITES:
- Proficient Programming Skills: Participants should have proficient programming skills in at least one programming language such as Python, Java, or similar.
- Experience with Python: Experience with Python programming language is essential, as the hands-on labs will be conducted using Python and popular machine learning libraries such as TensorFlow or PyTorch.
- Solid Understanding of Machine Learning: Participants should have a solid understanding of machine learning concepts, including supervised learning, unsupervised learning, neural networks, and training/validation/testing processes.
- Familiarity with Deep Learning Frameworks: Familiarity with deep learning frameworks such as TensorFlow or PyTorch is recommended, as participants will be using these frameworks to implement generative AI models.
Lab Requirements:
- Python Environment: Participants should have Python installed on their computers. They can install Python from the official Python website (https://www.python.org/) or using package managers like Anaconda or Miniconda.
- Integrated Development Environment (IDE): Participants can use any Python IDE of their choice for writing and running code. Recommended IDEs include PyCharm, Jupyter Notebook, and Google Colab.
- Machine Learning Libraries: Participants should have the necessary Python libraries installed for machine learning and deep learning. These include TensorFlow, Keras, PyTorch, and scikit-learn. They can install these libraries using pip or conda.
- GPU Support (Optional but Recommended): For running computationally intensive deep learning models, participants are encouraged to have access to a GPU-enabled environment. This can be achieved through cloud platforms like Google Cloud Platform (GCP), Amazon Web Services (AWS), or using GPU-enabled local machines.
Learning Path
- Day 1: Advanced Generative Models
- Overview of generative artificial intelligence and its applications
- Types of generative AI models: GANs, VAEs, and autoregressive models
- Understanding the differences between generative and discriminative models
- In-depth exploration of GANs architecture and training procedure
- Understanding the generator and discriminator networks
- Applications of GANs in image generation, style transfer, and data augmentation
- Introduction to variational autoencoders (VAEs) architecture and objective function
- Understanding reconstruction loss and KL divergence
- Applications of VAEs in image generation, anomaly detection, and dimensionality reduction
- Participants implement and train GAN and VAE models using TensorFlow or PyTorch
- Generating new images and analyzing latent space representations
- Fine-tuning hyperparameters and architecture for improved performance
- Day 2: Advanced Topics and Applications
- Overview of autoregressive models architecture and training procedure
- Understanding autoregressive models for sequential data generation
- Advanced techniques and architectures for generative modeling
- Introduction to conditional generative models such as conditional GANs and conditional VAEs
- Understanding conditional image generation and text-to-image synthesis
- Hands-on exercise: implementing conditional generative models for specialized tasks
- Exploring advanced applications of generative AI in various domains
- Case studies and examples of generative AI applications in healthcare, art, gaming, and more
- Emerging trends and future directions in generative AI research
- Participants work on advanced generative AI projects and applications
- Implementing custom generative models for specific use cases
- Experimenting with cutting-edge techniques and architectures in generative AI